nils-herrmann/clean_scirepeval huggingface dataset:
THE IMPACTS OF NATURAL DISASTERS ON THE POOR: A BACKGROUND NOTE [Submitted by Mary B. Anderson to Cornell University under Consulting Agreement Number 35113-9382 for use in development of the World Bank's WDR 2000/2001]

The term "natural disaster" is used to refer to catastrophes that arise from acts of nature such as flood, wind, drought and earthquake. However, many people have challenged this terminology as misleading. As long ago as 1976,1 Frederick Krimgold defined a disaster as an event that outstrips the capacity of a society to cope with it. This definition highlights the fact that not every natural crisis event becomes a disaster. Disasters occur only when a natural event damages or destroys human lives and property to such an extent that a society is disrupted. A high wind in an unpopulated area may fell trees and alter landscapes, but it is not considered a disaster if there is no human suffering or damage of property on which humans rely for their welfare. It is the interaction of acts of nature with human decisions and actions that result in disasters.
A Deep Normalization and Convolutional Neural Network for Image Smoke Detection

It is a challenging task to recognize smoke from images due to large variance of smoke color, texture, and shapes. There are smoke detection methods that have been proposed, but most of them are based on hand-crafted features. To improve the performance of smoke detection, we propose a novel deep normalization and convolutional neural network (DNCNN) with 14 layers to implement automatic feature extraction and classification. In DNCNN, traditional convolutional layers are replaced with normalization and convolutional layers to accelerate the training process and boost the performance of smoke detection. To reduce overfitting caused by imbalanced and insufficient training samples, we generate more training samples from original training data sets by using a variety of data enhancement techniques. Experimental results show that our method achieved very low false alarm rates below 0.60% with detection rates above 96.37% on our smoke data sets.
What makes a useful maturity model? a framework of general design principles for maturity models and its demonstration in business process management

Since the Software Engineering Institute has launch ed the Capability Maturity Model almost twenty years ago, hundreds of maturity models have been pr oposed by researchers and practitioners across multiple application domains. With process orientat ion being a central paradigm of organizational design and continuous process improvement taking to p positions on CIO agendas, maturity models are also prospering in business process management. Although the application of maturity models is increasing in quantity and breadth, the concept of maturity models is frequently subject to criticism. Indeed, numerous shortcomings have been disclosed r ef rring to both maturity models as design products and the process of maturity model design. Whereas research has already substantiated the design process, there is no holistic understanding of the principles of form and function – that is, t he design principles – maturity models should meet. We therefore propose a pragmatic, yet well-founded framework of general design principles justified by existing literature and grouped according to typical purposes of use. The framework is demonstrated using an exemplary set of maturity models related to business process management. We finally give a b rief outlook on implications and topics for further research.
The SECI model of knowledge creation : some empirical shortcomings

This paper presents a critique of key empirical aspects of Nonaka’s model of knowledge creation. If, as is now widely accepted, it is important to manage knowledge for a variety of reasons, then it is equally important that we have good models to assist this process. Nonaka and his colleagues’ model, in particular the SECI matrix of knowledge conversion, is increasingly being cited by authors in a widening set of disciplines, and has evidently achieved something like a paradigmatic status. However, as I shall argue below, it is not supported by empirical evidence, and some of the conversion modes are not coherent. The paradigmatic status of this theory may thus be unwarranted.
Fake currency detection using image processing

Currency duplication also is known as counterfeit currency is a vulnerable threat to Economy.it is now a common phenomenon due to advanced printing and scanning technology. Bangladesh has been facing the serious problem by increasing rate of fake note in the market.to get rid of this problem various fake not detection methods are available around the world and most of these are hardware based and costly.in the present paper an automated image-based technique is described for the detection of fake banknotes in Bangladesh. Security features of banknote such as watermark, micro-printing, and hologram etc., are extracted from the banknote images and then detection is performed using “Mathlab tools”
Use of exercise testing in the evaluation of interventional efficacy: an official ERS statement.

This document reviews 1) the measurement properties of commonly used exercise tests in patients with chronic respiratory diseases and 2) published studies on their utilty and/or evaluation obtained from MEDLINE and Cochrane Library searches between 1990 and March 2015.Exercise tests are reliable and consistently responsive to rehabilitative and pharmacological interventions. Thresholds for clinically important changes in performance are available for several tests. In pulmonary arterial hypertension, the 6-min walk test (6MWT), peak oxygen uptake and ventilation/carbon dioxide output indices appear to be the variables most responsive to vasodilators. While bronchodilators do not always show clinically relevant effects in chronic obstructive pulmonary disease, high-intensity constant work-rate (endurance) tests (CWRET) are considerably more responsive than incremental exercise tests and 6MWTs. High-intensity CWRETs need to be standardised to reduce interindividual variability. Additional physiological information and responsiveness can be obtained from isotime measurements, particularly of inspiratory capacity and dyspnoea. Less evidence is available for the endurance shuttle walk test. Although the incremental shuttle walk test and 6MWT are reliable and less expensive than cardiopulmonary exercise testing, two repetitions are needed at baseline. All exercise tests are safe when recommended precautions are followed, with evidence suggesting that no test is safer than others.
HOW IMPORTANT IS METHODOLOGY FOR THE ESTIMATES OF THE DETERMINANTS OF HAPPINESS?*

Psychologists and sociologists usually interpret happiness scores as cardinal and comparable across respondents, and thus run OLS regressions on happiness and changes in happiness. Economists usually assume only ordinality and have mainly used ordered latent response models, thereby not taking satisfactory account of fixed individual traits. We address this problem by developing a conditional estimator for the fixed-effect ordered logit model. We find that assuming ordinality or cardinality of happiness scores makes little difference, whilst allowing for fixed-effects does change results substantially. We call for more research into the determinants of the personality traits making up these fixed-effects.
A review of central 5-HT receptors and their function

It is now nearly 5 years since the last of the currently recognised 5-HT receptors was identified in terms of its cDNA sequence. Over this period, much effort has been directed towards understanding the function attributable to individual 5-HT receptors in the brain. This has been helped, in part, by the synthesis of a number of compounds that selectively interact with individual 5-HT receptor subtypes--although some 5-HT receptors still lack any selective ligands (e.g. 5-ht1E, 5-ht5A and 5-ht5B receptors). The present review provides background information for each 5-HT receptor subtype and subsequently reviews in more detail the functional responses attributed to each receptor in the brain. Clearly this latter area has moved forward in recent years and this progression is likely to continue given the level of interest associated with the actions of 5-HT. This interest is stimulated by the belief that pharmacological manipulation of the central 5-HT system will have therapeutic potential. In support of which, a number of 5-HT receptor ligands are currently utilised, or are in clinical development, to reduce the symptoms of CNS dysfunction.
Multi-Task Learning for Multiple Language Translation

In this paper, we investigate the problem of learning a machine translation model that can simultaneously translate sentences from one source language to multiple target languages. Our solution is inspired by the recently proposed neural machine translation model which generalizes machine translation as a sequence learning problem. We extend the neural machine translation to a multi-task learning framework which shares source language representation and separates the modeling of different target language translation. Our framework can be applied to situations where either large amounts of parallel data or limited parallel data is available. Experiments show that our multi-task learning model is able to achieve significantly higher translation quality over individually learned model in both situations on the data sets publicly available.
Connecting the Dots: Towards Human-Level Grammatical Error Correction

We build a grammatical error correction (GEC) system primarily based on the state-of-the-art statistical machine translation (SMT) approach, using task-specific features and tuning, and further enhance it with the modeling power of neural network joint models. The SMT-based system is weak in generalizing beyond patterns seen during training and lacks granularity below the word level. To address this issue, we incorporate a character-level SMT component targeting the misspelled words that the original SMT-based system fails to correct. Our final system achieves 53.14% F0.5 score on the benchmark CoNLL-2014 test set, an improvement of 3.62% F0.5 over the best previous published score.
Distributed MPI cluster with Docker Swarm mode

MPI is a well-established technology that is used widely in high-performance computing environment. However, setting up an MPI cluster can be challenging and time-consuming. This paper tackles this challenge by using modern containerization technology, which is Docker, and container orchestration technology, which is Docker Swarm mode, to automate the MPI cluster setup and deployment. We created a ready-to-use solution for developing and deploying MPI programs in a cluster of Docker containers running on multiple machines, orchestrated with Docker Swarm mode, to perform high computation tasks. We explain the considerations when creating Docker image that will be instantiated as MPI nodes, and we describe the steps needed to set up a fully connected MPI cluster as Docker containers running in a Docker Swarm mode. Our goal is to give the rationale behind our solution so that others can adapt to different system requirements. All pre-built Docker images, source code, documentation, and screencasts are publicly available.
Multi-Stream Multi-Class Fusion of Deep Networks for Video Classification

This paper studies deep network architectures to address the problem of video classification. A multi-stream framework is proposed to fully utilize the rich multimodal information in videos. Specifically, we first train three Convolutional Neural Networks to model spatial, short-term motion and audio clues respectively. Long Short Term Memory networks are then adopted to explore long-term temporal dynamics. With the outputs of the individual streams on multiple classes, we propose to mine class relationships hidden in the data from the trained models. The automatically discovered relationships are then leveraged in the multi-stream multi-class fusion process as a prior, indicating which and how much information is needed from the remaining classes, to adaptively determine the optimal fusion weights for generating the final scores of each class. Our contributions are two-fold. First, the multi-stream framework is able to exploit multimodal features that are more comprehensive than those previously attempted. Second, our proposed fusion method not only learns the best weights of the multiple network streams for each class, but also takes class relationship into account, which is known as a helpful clue in multi-class visual classification tasks. Our framework produces significantly better results than the state of the arts on two popular benchmarks, 92.2% on UCF-101 (without using audio) and 84.9% on Columbia Consumer Videos.
Is caffeine a cognitive enhancer?

The effects of caffeine on cognition were reviewed based on the large body of literature available on the topic. Caffeine does not usually affect performance in learning and memory tasks, although caffeine may occasionally have facilitatory or inhibitory effects on memory and learning. Caffeine facilitates learning in tasks in which information is presented passively; in tasks in which material is learned intentionally, caffeine has no effect. Caffeine facilitates performance in tasks involving working memory to a limited extent, but hinders performance in tasks that heavily depend on working memory, and caffeine appears to rather improve memory performance under suboptimal alertness conditions. Most studies, however, found improvements in reaction time. The ingestion of caffeine does not seem to affect long-term memory. At low doses, caffeine improves hedonic tone and reduces anxiety, while at high doses, there is an increase in tense arousal, including anxiety, nervousness, jitteriness. The larger improvement of performance in fatigued subjects confirms that caffeine is a mild stimulant. Caffeine has also been reported to prevent cognitive decline in healthy subjects but the results of the studies are heterogeneous, some finding no age-related effect while others reported effects only in one sex and mainly in the oldest population. In conclusion, it appears that caffeine cannot be considered a ;pure' cognitive enhancer. Its indirect action on arousal, mood and concentration contributes in large part to its cognitive enhancing properties.
Convolutional Sequence to Sequence Learning

The prevalent approach to sequence to sequence learning maps an input sequence to a variable length output sequence via recurrent neural networks. We introduce an architecture based entirely on convolutional neural  Compared to recurrent models, computations over all elements can be fully parallelized during training and optimization is easier since the number of non-linearities is fixed and independent of the input length. Our use of gated linear units eases gradient propagation and we equip each decoder layer with a separate attention module. We outperform the accuracy of the deep LSTM setup of Wu et al. (2016) on both WMT’14 EnglishGerman and WMT’14 English-French translation at an order of magnitude faster speed, both on GPU and CPU.
Visual Question Answering as Reading Comprehension

Visual question answering (VQA) demands simultaneous comprehension of both the image visual content and natural language questions. In some cases, the reasoning needs the help of common sense or general knowledge which usually appear in the form of text. Current methods jointly embed both the visual information and the textual feature into the same space. However, how to model the complex interactions between the two different modalities is not an easy task. In contrast to struggling on multimodal feature fusion, in this paper, we propose to unify all the input information by natural language so as to convert VQA into a machine reading comprehension problem. With this transformation, our method not only can tackle VQA datasets that focus on observation based questions, but can also be naturally extended to handle knowledge-based VQA which requires to explore large-scale external knowledge base. It is a step towards being able to exploit large volumes of text and natural language processing techniques to address VQA problem. Two types of models are proposed to deal with open-ended VQA and multiple-choice VQA respectively. We evaluate our models on three VQA benchmarks. The comparable performance with the state-of-the-art demonstrates the effectiveness of the proposed method.
Jumping the PBL Implementation Hurdle: Supporting the Efforts of K-12 Teachers.

While problem-based learning (PBL) has a relatively long history of successful use in medical and pre-professional schools, it has yet to be widely adopted by K–12 teachers. This may be due, in part, to the numerous challenges teachers experience when implementing PBL. In this paper, we describe specific hurdles that teachers are likely to encounter during the implementation process and provide specific suggestions for supporting teachers’ classroom efforts. Implementation challenges relate to 1) creating a culture of collaboration and interdependence, 2) adjusting to changing roles, and 3) scaffolding student learning and performance. By supporting teachers’ initial and ongoing efforts, we anticipate that more teachers will recognize the potential of PBL as an effective instructional approach for developing learners who are flexible thinkers and successful problem solvers.
Safe Policy Learning from Observations

In this paper, we consider the problem of learning a policy by observing numerous non-expert agents. Our goal is to extract a policy that, with high-confidence, acts better than the agents’ average performance. Such a setting is important for real-world problems where expert data is scarce but non-expert data can easily be obtained, e.g. by crowdsourcing. Our approach is to pose this problem as safe policy improvement in reinforcement learning. First, we evaluate an average behavior policy and approximate its value function. Then, we develop a stochastic policy improvement algorithm that safely improves the average behavior. The primary advantages of our approach, termed Rerouted Behavior Improvement (RBI), over other safe learning methods are its stability in the presence of value estimation errors and the elimination of a policy search process. We demonstrate these advantages in the Taxi grid-world domain and in four games from the Atari learning environment.
Redox environment of the cell as viewed through the redox state of the glutathione disulfide/glutathione couple.

Redox state is a term used widely in the research field of free radicals and oxidative stress. Unfortunately, it is used as a general term referring to relative changes that are not well defined or quantitated. In this review we provide a definition for the redox environment of biological fluids, cell organelles, cells, or tissue. We illustrate how the reduction potential of various redox couples can be estimated with the Nernst equation and show how pH and the concentrations of the species comprising different redox couples influence the reduction potential. We discuss how the redox state of the glutathione disulfide-glutathione couple (GSSG/2GSH) can serve as an important indicator of redox environment. There are many redox couples in a cell that work together to maintain the redox environment; the GSSG/2GSH couple is the most abundant redox couple in a cell. Changes of the half-cell reduction potential (E(hc)) of the GSSG/2GSH couple appear to correlate with the biological status of the cell: proliferation E(hc) approximately -240 mV; differentiation E(hc) approximately -200 mV; or apoptosis E(hc) approximately -170 mV. These estimates can be used to more fully understand the redox biochemistry that results from oxidative stress. These are the first steps toward a new quantitative biology, which hopefully will provide a rationale and understanding of the cellular mechanisms associated with cell growth and development, signaling, and reductive or oxidative stress.
Productive figurative communication : Conventional metaphors facilitate the comprehension of related novel metaphors

Three experiments explored whether conceptual mappings in conventional metaphors are productive, by testing whether the comprehension of novel metaphors was facilitated by first reading conceptually related conventional metaphors. The first experiment, a replication and extension of Keysar et al. [Keysar, B., Shen, Y., Glucksberg, S., Horton, W. (2000).  How metaphorical is it? Journal of Memory and Language 43, 576–593] (Experiment 2), found no such facilitation; however, in the second experiment, upon re-designing and improving the stimulus materials, facilitation was demonstrated. In a final experiment, this facilitation was shown to be specific to the conceptual mappings involved. The authors argue that metaphor productivity provides a communicative advantage and that this may be sufficient to explain the clustering of metaphors into families noted by Lakoff and Johnson [Lakoff & Johnson, M. (1980a). The metaphorical structure of the human conceptual system. Cognitive Science 4, 195–208].
Technology acceptance model (TAM) and social media usage: an empirical study on Facebook

Given the widespread popularity of social media, such as Twitter, Facebook, Googleþ , and LinkedIn, theorizing and understanding the user attitude and usage behavior of social media site is fundamental in developing future understandings and deployment of these new technologies. One approach to such studies on drivers of social media usage behavior would be to revisit the technology acceptance model (TAM). The purpose of this paper is to discuss these issues.  Decades of extensive research have focussed on validating the TAM, proposed by Davis (1986), for various types of information systems and communication technologies. TAM forecasts individual adoption and voluntary use of technology. This study examines individual adoption behavior of the most popular social networking site Facebook. The influences on the intention of using social networking based on individual’s perceived ease of use (EU), the user’s critical mass (CM), social networking site capability (CP), perceived playfulness (PP), trustworthiness (TW), and perceived usefulness (PU) is empirically examined with a primary data set of 398 users of Facebook gathered from a web-based questionnaire survey.  The results demonstrate that the revised social media TAM model proposed in this study supports all the hypotheses of social media usage behavior. The results of this study provide evidence for the importance of additional key variables to TAM in considering user engagement on social media sites and other social-media-related business strategies.  Based on our review of existing scientific literature on social media, few empirical studies have been conducted to scientifically evaluate and explain the usage behavior of social media using Facebook. A validated instrument of usage behavior of social media can provide usability experts and practitioners with a validated tool to assess social media acceptance and usage behavior. This can help us gain a better understanding of “who is and who is not using these sites, why and for what purposes” (Boyd and Ellison, 2007).