nils-herrmann/clean_scirepeval huggingface dataset:
Donor Retention in Online Crowdfunding Communities: A Case Study of DonorsChoose.org

Online crowdfunding platforms like DonorsChoose.org and Kickstarter allow specific projects to get funded by targeted contributions from a large number of people. Critical for the success of crowdfunding communities is recruitment and continued engagement of donors. With donor attrition rates above 70%, a significant challenge for online crowdfunding platforms as well as traditional offline non-profit organizations is the problem of donor retention. We present a large-scale study of millions of donors and donations on DonorsChoose.org, a crowdfunding platform for education projects. Studying an online crowdfunding platform allows for an unprecedented detailed view of how people direct their donations. We explore various factors impacting donor retention which allows us to identify different groups of donors and quantify their propensity to return for subsequent donations. We find that donors are more likely to return if they had a positive interaction with the receiver of the donation. We also show that this includes appropriate and timely recognition of their support as well as detailed communication of their impact. Finally, we discuss how our findings could inform steps to improve donor retention in crowdfunding communities and non-profit organizations.
Applications of Magnetic Water Technology in Farming and Agriculture Development : A Review of Recent Advances

Magnetic water treatment (MWT) techniques have shown promising potentials in different areas specially agriculture. Safety, compatibility and simplicity, environmentally friendliness, low operating cost and not proven harmful effects are the main advantages of electromagnetic field (EMF) over conventional methods for water treatment. Magnetized or magnetic water (MW) possesses unique physical and chemical characteristics making it a multi-purpose compound with potential benefits in medical treatment, industrial as well as environmental applications. The unique physical and electrochemical characteristics of MW have attracted research interests to develop different devices and techniques in agricultural and environmental applications. Improvements of irrigation water quality and quantity, crop yields and quality, soil improvement and water saving are some of the reported benefits of MWT in agriculture. In addition, magnetic field treatments have shown beneficial effects on the germination of seeds, plant growth and development, the ripening and yield of field crops. The main challenge in applications of MW in agriculture is efficient integrating of irrigation components, designing suitable pumps compatible with technical and field requirements of magnetic MWT systems. The present study reviews the applications of MW in agricultures. The practical challenges in using MW as well as future perspective are discussed.
Graph embedding techniques, applications, and performance: A survey

Graphs, such as social networks, word co-occurrence networks, and communication networks, occur naturally in various real-world applications. Analyzing them yields insight into the structure of society, language, and different patterns of communication. Many approaches have been proposed to perform the analysis. Recently, methods which use the representation of graph nodes in vector space have gained traction from the research community. In this survey, we provide a comprehensive and structured analysis of various graph embedding techniques proposed in the literature. We first introduce the embedding task and its challenges such as scalability, choice of dimensionality, and features to be preserved, and their possible solutions. We then present three categories of approaches based on factorization methods, random walks, and deep learning, with examples of representative algorithms in each category and analysis of their performance on various tasks. We evaluate these state-of-the-art methods on a few common datasets and compare their performance against one another and versus non-embedding based models. Our analysis concludes by suggesting some potential applications and future directions. We finally present the open-source Python library, named GEM (Graph Embedding Methods), we developed that provides all presented algorithms within a unified interface, to foster and facilitate research on the topic.
Can cascades be predicted?

On many social networking web sites such as Facebook and Twitter, resharing or reposting functionality allows users to share others' content with their own friends or followers. As content is reshared from user to user, large cascades of reshares can form. While a growing body of research has focused on analyzing and characterizing such cascades, a recent, parallel line of work has argued that the future trajectory of a cascade may be inherently unpredictable. In this work, we develop a framework for addressing cascade prediction problems. On a large sample of photo reshare cascades on Facebook, we find strong performance in predicting whether a cascade will continue to grow in the future. We find that the relative growth of a cascade becomes more predictable as we observe more of its reshares, that temporal and structural features are key predictors of cascade size, and that initially, breadth, rather than depth in a cascade is a better indicator of larger cascades. This prediction performance is robust in the sense that multiple distinct classes of features all achieve similar performance. We also discover that temporal features are predictive of a cascade's eventual shape. Observing independent cascades of the same content, we find that while these cascades differ greatly in size, we are still able to predict which ends up the largest.
Can the Blockchain Technology Solve Trust Issues in Industrial Networks?

Since its inception about a decade ago, practitioners and researchers alike have been drawn to the blockchain technology vibe. Advocates of blockchain argue that the technology is taking us to truly ‘trust-free’ transactions. A long list of applications of blockchain has also been proposed in a relatively short period of time. However, a closer look into the literature reveals two shortcomings. To start with, the substantial proportion of the research on blockchain has focused on addressing the technical aspects of blockchain—design and features— as well as legal issues. However, there is a lack of knowledge on how blockchain technology can be used to solve practical problems faced by organizations in different sectors and industries—measurement and value, trust, management and organization. The state-of-the-art also shows that there is a dominance of conceptual and design-oriented research paradigms. To address this gap and respond to the calls for further research, this paper presents a research plan for a longitudinal case study to investigate whether blockchain technology can affect the way organizations conduct their business relationships.
A survey of the use of crowdsourcing in software engineering

The term ‘crowdsourcing’ was initially introduced in 2006 to describe an emerging distributed problem-solving model by online workers. Since then it has been widely studied and practiced to support software engineering. In this paper we provide a comprehensive survey of the use of crowdsourcing in software engineering, seeking to cover all literature on this topic. We first review the definitions of crowdsourcing and derive our definition of Crowdsourcing Software Engineering together with its taxonomy. Then we summarise industrial crowdsourcing practice in software engineering and corresponding case studies. We further analyse the software engineering domains, tasks and applications for crowdsourcing and the platforms and stakeholders involved in realising Crowdsourced Software Engineering solutions. We conclude by exposing trends, open issues and opportunities for future research on Crowdsourced Software Engineering.
Design and Implementation of PCB Inductors With Litz-Wire Structure for Conventional-Size Large-Signal Domestic Induction Heating Applications

Printed circuit board (PCB) implementations provide high repeatability and ease of manufacturing as well as potential cost savings for many applications. An optimization procedure is proposed for designing PCB inductors for use in domestic induction cooktops. Cables for classical inductors are built of multistranded round litz wire for efficiency reasons. A planar litz structure in a PCB has been applied to achieve a similar performance to that of traditional constructions (in terms of maximum output power, inductive efficiency, and thermal behavior). The inductor performance has been optimized by a finite-element-analysis-tool-based method taking into account the power losses in rectangular cross-sectional tracks of the cable as well as the geometrical constraints of PCB implementations and the requirements of the associated electronics. Finally, the proposed method has been validated by means of experimental measurements under large-signal conditions on a PCB inductor prototype.
FSO-Based Vertical Backhaul/Fronthaul Framework for 5G+ Wireless Networks

The presence of a super high rate, but also cost-efficient, easy-to-deploy, and scalable, backhaul/ fronthaul framework, is essential in the upcoming 5G wireless networks and beyond. Motivated by the mounting interest in unmanned flying platforms of various types, including UAVs, drones, balloons, and HAPs/MAPs/LAPs, which we refer to as networked flying platforms (NFPs), for providing communications services, and by the recent advances in free space optics (FSO), this article investigates the feasibility of a novel vertical backhaul/fronthaul framework where the NFPs transport the backhaul/fronthaul traffic between the access and core networks via pointto- point FSO links. The performance of the proposed innovative approach is investigated under different weather conditions and a broad range of system parameters. Simulation results demonstrate that the FSO-based vertical backhaul/ fronthaul framework can offer data rates higher than the baseline alternatives, and thus can be considered a promising solution to the emerging backhaul/fronthaul requirements of the 5G+ wireless networks, particularly in the presence of ultra-dense heterogeneous small cells. This article also presents the challenges that accompany such a novel framework and provides some key ideas toward overcoming these challenges.
Testing of a Cognitive-Behavioral Model of Pathological Internet Use

Davis (2001) developed a cognitive-behavioral model of pathological Internet use (PIU) in which the availability, and awareness of the Internet, psychopathologies such as depression, social anxiety or substance abuse, and situational cues providing reinforcement of Internet usage behaviors, interact to produce maladaptive cognitions(Charlton & Danforth, 2007). This model posits that user's cognition is responsible for PIU, and ineffective and/or behavioral symptoms in turn. To date, there has been no comprehensive study to test Davis' model. Lee, Choi et al. (2007) took the idea of cognitivebehavioral perspective (Davis, 2001) as an approach to developing tests of behavioral symptoms and negative outcomes of PIU respectively. Since their work was focused on developing the two tests, building or testing models of explicating PIU was not the main task. Thus, we attempted to reanalyze their data and to empirically explore and test a model of PIU.
Cloud Computing-A Study of Infrastructure as a Service Sukhdev

Cloud computing is a promising technology of the present and the future which uses the grid computing as its backbone. Cloud computing is the hottest topic of information and communication technology (ICT) for implementing it for individual, communities and business. It provides services such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). It provides many benefits like scalability and pay per use etc. Different deployment models are also available to fulfill the needs of the business and industry.  Cloud computing, Network, Model, Infrastructure, Platform
A Food Recognition System for Diabetic Patients Based on an Optimized Bag-of-Features Model

Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the bag-of-features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.
Health information on the Internet: accessibility, quality, and readability in English and Spanish.

Despite the substantial amount of health-related information available on the Internet, little is known about the accessibility, quality, and reading grade level of that health information.



To evaluate health information on breast cancer, depression, obesity, and childhood asthma available through English- and Spanish-language search engines and Web sites.



Three unique studies were performed from July 2000 through December 2000. Accessibility of 14 search engines was assessed using a structured search experiment. Quality of 25 health Web sites and content provided by 1 search engine was evaluated by 34 physicians using structured implicit review (interrater reliability >0.90). The reading grade level of text selected for structured implicit review was established using the Fry Readability Graph method.



For the accessibility study, proportion of links leading to relevant content; for quality, coverage and accuracy of key clinical elements; and grade level reading formulas.



Less than one quarter of the search engine's first pages of links led to relevant content (20% of English and 12% of Spanish). On average, 45% of the clinical elements on English- and 22% on Spanish-language Web sites were more than minimally covered and completely accurate and 24% of the clinical elements on English- and 53% on Spanish-language Web sites were not covered at all. All English and 86% of Spanish Web sites required high school level or greater reading ability.



Accessing health information using search engines and simple search terms is not efficient. Coverage of key information on English- and Spanish-language Web sites is poor and inconsistent, although the accuracy of the information provided is generally good. High reading levels are required to comprehend Web-based health information.
Academic Writing and Culture : An Overview of Differences between English , French and German

This article provides a critical overview of recent research on cross-cultural divergences between English, French and German academic writing, demonstrating its relevance to translation. The author starts by discussing Galtung’s notion of culture-specific intellectual styles. He then explores the relationship between composition teaching and writing style. This is followed by a detailed discussion of cross-language comparisons of various text types which lend evidence to significant differences between the linguacultures under survey. The resultant plurality of linguacultures, the author goes on to argue, must be preserved as a value in itself, and merits special attention on the part of the translator.
Creative Activity, Personality, Mental Illness, and ShortTerm Mating Success

It has been argued that creativity evolved, at least in part, through sexual selection to attract mates. Recent research lends support to this view and has also demonstrated a link between certain dimensions of schizotypy, creativity, and short-term mating. The current study delves deeper into these relationships by focusing on engagement in creative activity and employing an expansive set of personality and mental health measures (Five Factor Model, schizotypy, anxiety, and depression). A general tendency to engage in everyday forms of creative activity was related to number of sexual partners within the past year in males only. Furthermore, schizotypy, anxiety, and Neuroticism were all indirectly related to short-term mating success, again for males only. The study provides additional support for predictions made by sexual selection theory that men have a higher drive for creative display, and that creativity is linked with higher short-term mating success. The study also provides support for the contention that certain forms of mental illness may still exist in the gene pool because particular personality traits associated with milder forms of mental illness (i.e., Neuroticism & schizotypy) are also associated directly with creativity and indirectly with short-term mating success.
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Networks (StackGANs) aimed at generating high-resolution photo-realistic images. First, we propose a two-stage generative adversarial network architecture, StackGAN-v1, for text-to-image synthesis. The Stage-I GAN sketches the primitive shape and colors of a scene based on a given text description, yielding low-resolution images. The Stage-II GAN takes Stage-I results and the text description as inputs, and generates high-resolution images with photo-realistic details. Second, an advanced multi-stage generative adversarial network architecture, StackGAN-v2, is proposed for both conditional and unconditional generative tasks. Our StackGAN-v2 consists of multiple generators and multiple discriminators arranged in a tree-like structure; images at multiple scales corresponding to the same scene are generated from different branches of the tree.  shows more stable training behavior than StackGAN-v1 by jointly approximating multiple distributions. Extensive experiments demonstrate that the proposed stacked generative adversarial networks significantly outperform other state-of-the-art methods in generating photo-realistic images.
Mining Facebook Data for Predictive Personality Modeling

Beyond being facilitators of human interactions, social networks have become an interesting target of research, providing rich information for studying and modeling user’s behavior. Identification of personality-related indicators encrypted in Facebook profiles and activities are of special concern in our current research efforts. This paper explores the feasibility of modeling user personality based on a proposed set of features extracted from the Facebook data. The encouraging results of our study, exploring the suitability and performance of several classification techniques, will also be presented.
Writing for publication: the basics.

Most midwives and nurses do not write for publication. Previous authors on this topic have focussed on the processes of writing and getting published. Although definitive English usage style guides exist, they are infrequently consulted by new midwifery authors.



To enable new writers to confidently apply the basic skills of scientific writing when preparing a paper for publication.


OVERVIEW
The basic skills needed for scientific writing are the focus of this paper. The importance of careful word choices is discussed first. Next, the skills of writing sentences are presented. Finally, the skills of writing paragraphs are discussed. Examples of poor and better writing are given in relation to each of these basic elements.
Cannabis Pharmacology: The Usual Suspects and a Few Promising Leads.

The golden age of cannabis pharmacology began in the 1960s as Raphael Mechoulam and his colleagues in Israel isolated and synthesized cannabidiol, tetrahydrocannabinol, and other phytocannabinoids. Initially, THC garnered most research interest with sporadic attention to cannabidiol, which has only rekindled in the last 15 years through a demonstration of its remarkably versatile pharmacology and synergy with THC. Gradually a cognizance of the potential of other phytocannabinoids has developed. Contemporaneous assessment of cannabis pharmacology must be even far more inclusive. Medical and recreational consumers alike have long believed in unique attributes of certain cannabis chemovars despite their similarity in cannabinoid profiles. This has focused additional research on the pharmacological contributions of mono- and sesquiterpenoids to the effects of cannabis flower preparations. Investigation reveals these aromatic compounds to contribute modulatory and therapeutic roles in the cannabis entourage far beyond expectations considering their modest concentrations in the plant. Synergistic relationships of the terpenoids to cannabinoids will be highlighted and include many complementary roles to boost therapeutic efficacy in treatment of pain, psychiatric disorders, cancer, and numerous other areas. Additional parts of the cannabis plant provide a wide and distinct variety of other compounds of pharmacological interest, including the triterpenoid friedelin from the roots, canniprene from the fan leaves, cannabisin from seed coats, and cannflavin A from seed sprouts. This chapter will explore the unique attributes of these agents and demonstrate how cannabis may yet fulfil its potential as Mechoulam's professed "pharmacological treasure trove."
Quantum generative adversarial networks

Quantum machine learning is expected to be one of the first potential general-purpose applications of near-term quantum devices. A major recent breakthrough in classical machine learning is the notion of generative adversarial training, where the gradients of a discriminator model are used to train a separate generative model. In this work and a companion paper, we extend adversarial training to the quantum domain and show how to construct generative adversarial networks using quantum circuits. Furthermore, we also show how to compute gradients – a key element in generative adversarial network training – using another quantum circuit. We give an example of a simple practical circuit ansatz to parametrize quantum machine learning models and perform a simple numerical experiment to demonstrate that quantum generative adversarial networks can be trained successfully.