nils-herrmann/clean_scirepeval huggingface dataset:
Learning to Compare: Relation Network for Few-Shot Learning

We present a conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each. Our method, called the Relation Network (RN), is trained end-to-end from scratch. During meta-learning, it learns to learn a deep distance metric to compare a small number of images within episodes, each of which is designed to simulate the few-shot setting. Once trained, a RN is able to classify images of new classes by computing relation scores between query images and the few examples of each new class without further updating the network. Besides providing improved performance on few-shot learning, our framework is easily extended to zero-shot learning. Extensive experiments on five benchmarks demonstrate that our simple approach provides a unified and effective approach for both of these two tasks.
Marketing on the Internet - who can benefit from an online marketing approach?

The research builds upon the literature in electronic commerce and past research in marketing with the objective of understanding factors that impact a product’s adaptability to online marketing. A review of marketing channel choice literature reveals a set of factors and channel choice functions that are considered important in making channel decisions. Using this as a basis, four major channel functions, namely, product customization, availability, logistics, and transaction complexity are considered relevant in understanding the implications for Internet marketing. By building upon previous research in the area of channel selection, we provide a means of classifying Internet marketing initiatives based on product characteristics. The classification scheme based on product characteristics can help analyze the significance of each factor on the success of a firm’s online marketing approach. Further, the classification scheme is used to discuss decision support implications. q 2000 Elsevier Science B.V. All rights reserved.
Technological and institutional ‘ lock-in ’ as a barrier to sustainable innovation

This paper examines the role of technological and institutional ‘lock-in’ as a barrier to more sustainable innovation, and the implications of this for determining appropriate policy responses. It begins by reviewing the idea of lock-in, and recent work applying this concept to carbon-based energy systems. It then summarises work by the author and colleagues outlining policy instruments to promote more sustainable innovation. Finally, it introduces a new project that will analyse how to determine an appropriate mix of such instruments to overcome the current carbon lock-in.
Auto-Encoder Guided GAN for Chinese Calligraphy Synthesis

In this paper, we investigate the Chinese calligraphy synthesis problem: synthesizing Chinese calligraphy images with specified style from standard font(eg. Hei font) images ). Recent works mostly follow the stroke extraction and assemble pipeline which is complex in the process and limited by the effect of stroke extraction. In this work we treat the calligraphy synthesis problem as an image-to-image translation problem and propose a deep neural network based model which can generate calligraphy images from standard font images directly. Besides, we also construct a large scale benchmark that contains various styles for Chinese calligraphy synthesis. We evaluate our method as well as some baseline methods on the proposed dataset, and the experimental results demonstrate the effectiveness of our proposed model.
Multimodal Popularity Prediction of Brand-related Social Media Posts

Brand-related user posts on social networks are growing at a staggering rate, where users express their opinions about brands by sharing multimodal posts. However, while some posts become popular, others are ignored. In this paper, we present an approach for identifying what aspects of posts determine their popularity. We hypothesize that brand-related posts may be popular due to several cues related to factual information, sentiment, vividness and entertainment parameters about the brand. We call the ensemble of cues engagement parameters. In our approach, we propose to use these parameters for predicting brand-related user post popularity. Experiments on a collection of fast food brand-related user posts crawled from Instagram show that: visual and textual features are complementary in predicting the popularity of a post; predicting popularity using our proposed engagement parameters is more accurate than predicting popularity directly from visual and textual features; and our proposed approach makes it possible to understand what drives post popularity in general as well as isolate the brand specific drivers.
A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data

Understanding unstructured text is a major goal within natural language processing. Comprehension tests pose questions based on short text passages to evaluate such understanding. In this work, we investigate machine comprehension on the challenging MCTest benchmark. Partly because of its limited size, prior work on MCTest has focused mainly on engineering better features. We tackle the dataset with a neural approach, harnessing simple neural networks arranged in a parallel hierarchy. The parallel hierarchy enables our model to compare the passage, question, and answer from a variety of trainable perspectives, as opposed to using a manually designed, rigid feature set. Perspectives range from the word level to sentence fragments to sequences of sentences; the networks operate only on word-embedding representations of text. When trained with a methodology designed to help cope with limited training data, our Parallel-Hierarchical model sets a new state of the art for MCTest, outperforming previous feature-engineered approaches slightly and previous neural approaches by a significant margin (over 15% absolute).
Climate variability and vulnerability to climate change: a review

The focus of the great majority of climate change impact studies is on changes in mean climate. In terms of climate model output, these changes are more robust than changes in climate variability. By concentrating on changes in climate means, the full impacts of climate change on biological and human systems are probably being seriously underestimated. Here, we briefly review the possible impacts of changes in climate variability and the frequency of extreme events on biological and food systems, with a focus on the developing world. We present new analysis that tentatively links increases in climate variability with increasing food insecurity in the future. We consider the ways in which people deal with climate variability and extremes and how they may adapt in the future. Key knowledge and data gaps are highlighted. These include the timing and interactions of different climatic stresses on plant growth and development, particularly at higher temperatures, and the impacts on crops, livestock and farming systems of changes in climate variability and extreme events on pest-weed-disease complexes. We highlight the need to reframe research questions in such a way that they can provide decision makers throughout the food system with actionable answers, and the need for investment in climate and environmental monitoring. Improved understanding of the full range of impacts of climate change on biological and food systems is a critical step in being able to address effectively the effects of climate variability and extreme events on human vulnerability and food security, particularly in agriculturally based developing countries facing the challenge of having to feed rapidly growing populations in the coming decades.
Harnessing the rhizosphere microbiome through plant breeding and agricultural management

The need to enhance the sustainability of intensive agricultural systems is widely recognized One promising approach is to encourage beneficial services provided by soil microorganisms to decrease the inputs of fertilizers and pesticides. However, limited success of this approach in field applications raises questions as to how this might be best accomplished. We highlight connections between root exudates and the rhizosphere microbiome, and discuss the possibility of using plant exudation characteristics to selectively enhance beneficial microbial activities and microbiome characteristics. Gaps in our understanding and areas of research that are vital to our ability to more fully exploit the soil microbiome for agroecosystem productivity and sustainability are also discussed. This article outlines strategies for more effectively exploiting beneficial microbial services on agricultural systems, and cals attention to topics that require additional research.
Generative Adversarial Nets

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1 2 everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.
The Pedagogical Potential of Augmented Reality Apps

Experimental research in the field of augmented reality has increased in recent years. So has investment and interest among the education industry. This manuscript analyses the current state of augmented reality applications for stationary, mobile, and wearable devices. Specifically, we will highlight the areas within education that have been positively impacted, and provide a short list of outstanding apps. Also, we specify the major technical components of augmented reality applications. Alignment with solid educational theory will promote the effectiveness of the new technology. Several barriers to the full integration of augmented reality into education are discussed. Finally, using our understanding of educational technology as a lens, we project the future state of augmented reality in pedagogy. We conclude that augmented reality has been proven effective among multiple subject matters. This technology will soon become pervasive not only in education, but in society in general.
Quality Assessment of TanDEM-X Raw DEMs Oriented to a Fusion with CartoSAT-1 DEMs

This paper addresses a quality assessment of TanDEM-X standard raw DEMs, with a resolution of 12 meters, for two different terrain configurations: urban areas and moderate topography. The analysis is performed in the geospatial domain. Beside TanDEM-X, the same analysis is also carried out for CartoSAT-1 and LiDAR DEMs. The latter one is used as a reference. Nevertheless, the focus is centered on TanDEM-X, whose geometric limitations and their impacts on the DEM are analyzed here in detail. How the DEM appears in layover, shadow and phase unwrapping error areas is one of the objectives of the paper. The chosen test site is around Terrassa/Barcelona (Spain), offering all kinds of terrain variations. The final scope of the analysis is to learn about the potentials and the limitations of the two systems, radar (TanDEM-X) and optical (CartoSAT-1), in a way to optimally fuse them and to create an enhanced DEM. A simple fusion processing chain, based on a weighted average depending on the quality of the DEMs adapted to the local geometry, is tested. First results show that in urban areas the improvements are limited, mainly due to the previously analyzed geometrical issues, whereas in moderate terrain areas the enhancement is significant, with a drop in the RMSE of about 25% for TanDEM-X and 30% for CartoSAT-1.
Requirements Engineering as a Success Factor in Software Projects

Most RE research is conceptual and concentrates on methods or techniques, primarily supporting a single activity. Moreover, the rare field studies we actually have do not establish a link between RE practices and performance. We therefore conducted this study to identify the RE practices that clearly contribute to software project success. Stakeholders and teams Stakeholders are individuals and organizations that are actively involved in a software project or whose interests the project affects. Stakeholders of any computer system can include customers, users, project managers, analysts , developers, senior management, and quality assurance staff. Table 1 illustrates the wide range of expertise and motivations that stakeholders typically exhibit. 2 A typical software project team consists of a project manager, analysts, developers, and quality assurance personnel. Often it includes users or their representatives. In the case of commercial off-the-shelf (COTS) software, marketers such as sales representatives and account managers tend to substitute for users and customers. Field study Seven field studies have reported on RE in practice. 3–9 Unfortunately, these rare studies have not established a clear link to performance and tend to focus on a narrow set of variables. Our study provides a more integrated view of RE by investigating team knowledge, allocated resources, and deployed RE processes  and their contribution to project success. In addition, we incorporate the observations of previous field studies. Fifteen RE teams, including six COTS and nine customized application develop-Based on their field study of 15 requirements engineering teams, the authors identify the RE practices that clearly contribute to project success, particularly in terms of team knowledge, resource allocation, and process. D eficient requirements are the single biggest cause of software project failure. From studying several hundred organizations, Capers Jones discovered that RE is deficient in more than 75 percent of all enterprises. 1 In other words, getting requirements right might be the single most important and difficult part of a software project. Despite its importance, we know surprisingly little about the actual process of specifying software. " The RE Process " sidebar provides a basic description.
Event Detection and Co-reference with Minimal Supervision

An important aspect of natural language understanding involves recognizing and categorizing events and the relations among them. However, these tasks are quite subtle and annotating training data for machine learning based approaches is an expensive task, resulting in supervised systems that attempt to learn complex models from small amounts of data, which they over-fit. This paper addresses this challenge by developing an event detection and co-reference system with minimal supervision, in the form of a few event examples. We view these tasks as semantic similarity problems between event mentions or event mentions and an ontology of types, thus facilitating the use of large amounts of out of domain text data. Notably, our semantic relatedness function exploits the structure of the text by making use of a semantic-role-labeling based representation of an event. We show that our approach to event detection is competitive with the top supervised methods. More significantly, we outperform stateof-the-art supervised methods for event coreference on benchmark data sets, and support significantly better transfer across domains.
Physical activity and public health in older adults: recommendation from the American College of Sports Medicine and the American Heart Association.

To issue a recommendation on the types and amounts of physical activity needed to improve and maintain health in older adults.



A panel of scientists with expertise in public health, behavioral science, epidemiology, exercise science, medicine, and gerontology.



The expert panel reviewed existing consensus statements and relevant evidence from primary research articles and reviews of the literature.


PROCESS
After drafting a recommendation for the older adult population and reviewing drafts of the Updated Recommendation from the American College of Sports Medicine (ACSM) and the American Heart Association (AHA) for Adults, the panel issued a final recommendation on physical activity for older adults.



The recommendation for older adults is similar to the updated ACSM/AHA recommendation for adults, but has several important differences including: the recommended intensity of aerobic activity takes into account the older adult's aerobic fitness; activities that maintain or increase flexibility are recommended; and balance exercises are recommended for older adults at risk of falls. In addition, older adults should have an activity plan for achieving recommended physical activity that integrates preventive and therapeutic recommendations. The promotion of physical activity in older adults should emphasize moderate-intensity aerobic activity, muscle-strengthening activity, reducing sedentary behavior, and risk management.
Oblivious Neural Network Computing via Homomorphic Encryption

The problem of secure data processing by means of a neural network (NN) is addressed. Secure processing refers to the possibility that the NN owner does not get any knowledge about the processed data since they are provided to him in encrypted format. At the same time, the NN itself is protected, given that its owner may not be willing to disclose the knowledge embedded within it. The considered level of protection ensures that the data provided to the network and the network weights and activation functions are kept secret. Particular attention is given to prevent any disclosure of information that could bring a malevolent user to get access to the NN secrets by properly inputting fake data to any point of the proposed protocol. With respect to previous works in this field, the interaction between the user and the NN owner is kept to a minimum with no resort to multiparty computation protocols.
Comparison of behavioral intervention and sensory-integration therapy in the treatment of challenging behavior.

The objective of the current study was to compare the effects of sensory-integration therapy (SIT) and a behavioral intervention on rates of challenging behavior (including self-injurious behavior) in four children diagnosed with Autism Spectrum Disorder. For each of the participants a functional assessment was conducted to identify the variables maintaining challenging behavior. Results of these assessments were used to design function-based behavioral interventions for each participant. Recommendations for the sensory-integration treatment were designed by an Occupational Therapist, trained in the use of sensory-integration theory and techniques. The sensory-integration techniques were not dependent on the results of the functional assessments. The study was conducted within an alternating treatments design, with initial baseline and final best treatment phase. For each participant, results demonstrated that the behavioral intervention was more effective than the sensory integration therapy in the treatment of challenging behavior. In the best treatment phase, the behavioral intervention alone was implemented and further reduction was observed in the rate of challenging behavior. Analysis of saliva samples revealed relatively low levels of cortisol and very little stress-responsivity across the SIT condition and the behavioral intervention condition, which may be related to the participants' capacity to perceive stress in terms of its social significance.
The Effect of Customer Value and Satisfaction on Customer Loyalty : The Moderating Role of Ethical Reputation

Today, creating value for customers and satisfaction and loyalty is an important problem for organization to be winner or loser in market. Strengthening the relationship between patient and hospital customer satisfaction grows and it also increases the value of the relationships between the parties. This is what will be leading to higher loyalty and then increasing in customers behavioral intentions, positive attitude, trust and commitment. The objective of the study was to investigate the effect of customer value and satisfaction on customer loyalty and the moderating role of ethical reputation, a sample size of 427 cases was selected using random sampling. Data were collected with questionnaire instrument from the study of Jay and Jaramillo (2011) and Athanassopoulos (2001). Model was developed and tested with structural equation model used PLS software. The results show that customer value has a positive effect on customer satisfaction and customer loyalty and customer satisfaction has a positive effect on customer loyalty and eventually ethical reputation positively moderates the relationship between customer value and customer satisfaction.
Pattern-revising Enhanced Simple Question Answering over Knowledge Bases

Answering over Knowledge Bases (KB-QA), which automatically answer natural language questions based on the facts contained by a knowledge base, is one of the most important natural language processing (NLP) tasks. Simple questions constitute a large part of questions queried on the web, still being a challenge to QA systems. In this work, we propose to conduct pattern extraction and entity linking first, and put forward pattern revising procedure to mitigate the error propagation problem. In order to learn to rank candidate subject-predicate pairs to enable the relevant facts retrieval given a question, we propose to do joint fact selection enhanced by relation detection. Multi-level encodings and multi-dimension information are leveraged to strengthen the whole procedure. The experimental results demonstrate that our approach sets a new record in this task, outperforming the current state-of-the-art by an absolute large margin.
Creativity and / or Alertness : A Reconsideration of the Schumpeterian

T he purpose of this paper is to reconsider the difference between Schumpeter’s portrayal of the entrepreneurial role, and my own earlier (1969, 1973) portrayal of that same role. 1 In 1969 and in 1973, in the course of developing my own understanding of the entrepreneurial character of the competitive, equilibrative market process, I emphasized these differences as I then saw them. Schumpeter’s entrepreneur, I pointed out, was essentially disruptive, destroying the pre-existing state of equilibrium. My entrepreneur, on the other hand, was responsible for the tendency through which initial conditions of disequilibrium come systematically to be displaced by equilibrative market competition. The outcome of the present reconsideration will be, not a thoroughgoing “reconciliation” of these two conceptions of the entrepreneurial role—I still believe that these views are, at least in part, contrasting ones—but a clearer understanding of how each of these apparently conflicting views can be seen as plausible and realistic; and how each can usefully advance economic understanding (of respectively different aspects of the capitalist economy). The central theme of this reconsideration can be expressed in the following four propositions: