nils-herrmann/clean_scirepeval huggingface dataset:
Mineable or messy? Assessing the quality of macro-level tourism information derived from social media

Social media are a primary means for travelers to connect with each other and plan trips. They can also help tourism suppliers (e.g., by providing relevant information), thus overcoming the shortcomings of traditional information sources. User-generated content from social media has already been used in many studies as a primary information source. However, the quality of information derived thus far remains largely unclear. This study assesses the quality of macro-level information on the spatio-temporal distribution of tourism derived from online travel reviews in social media in terms of completeness, timeliness, and accuracy. We found that information quality increased from 2000 to 2009 as online travel reviews increasingly covered more countries, became available earlier than statistics reported by the United Nations World Tourism Organization (UNWTO), were highly correlated with the UNWTO statistics. We conclude that social media are a good information source for macro-level spatio-temporal tourism information and could be used, for example, to estimate tourism figures.
Neural Discourse Modeling of Conversations

Deep neural networks have shown recent promise in many language-related tasks such as the modeling of conversations. We extend RNN-based sequence to sequence models to capture the long range discourse across many turns of conversation. We perform a sensitivity analysis on how much additional context affects performance, and provide quantitative and qualitative evidence that these models are able to capture discourse relationships across multiple utterances. Our results quantifies how adding an additional RNN layer for modeling discourse improves the quality of output utterances and providing more of the previous conversation as input also improves performance. By searching the generated outputs for specific discourse markers we show how neural discourse models can exhibit increased coherence and cohesion in conversations.
A review on traditional Turkish fermented non-alcoholic beverages: microbiota, fermentation process and quality characteristics.

Shalgam juice, hardaliye, boza, ayran (yoghurt drink) and kefir are the most known traditional Turkish fermented non-alcoholic beverages. The first three are obtained from vegetables, fruits and cereals, and the last two ones are made of milk. Shalgam juice, hardaliye and ayran are produced by lactic acid fermentation. Their microbiota is mainly composed of lactic acid bacteria (LAB). Lactobacillus plantarum, Lactobacillus brevis and Lactobacillus paracasei subsp. paracasei in shalgam fermentation and L. paracasei subsp. paracasei and Lactobacillus casei subsp. pseudoplantarum in hardaliye fermentation are predominant. Ayran is traditionally prepared by mixing yoghurt with water and salt. Yoghurt starter cultures are used in industrial ayran production. On the other hand, both alcohol and lactic acid fermentation occur in boza and kefir. Boza is prepared by using a mixture of maize, wheat and rice or their flours and water. Generally previously produced boza or sourdough/yoghurt are used as starter culture which is rich in Lactobacillus spp. and yeasts. Kefir is prepared by inoculation of raw milk with kefir grains which consists of different species of yeasts, LAB, acetic acid bacteria in a protein and polysaccharide matrix. The microbiota of boza and kefir is affected from raw materials, the origin and the production methods. In this review, physicochemical properties, manufacturing technologies, microbiota and shelf life and spoilage of traditional fermented beverages were summarized along with how fermentation conditions could affect rheological properties of end product which are important during processing and storage.
A Deep Neural Model Of Emotion Appraisal

Emotional concepts play a huge role in our daily life since they take part into many cognitive processes: from the perception of the environment around us to different learning processes and natural communication. Social robots need to communicate with humans, which increased also the popularity of affective embodied models that adopt different emotional concepts in many everyday tasks. However, there is still a gap between the development of these solutions and the integration and development of a complex emotion appraisal system, which is much necessary for true social robots. In this paper, we propose a deep neural model which is designed in the light of different aspects of developmental learning of emotional concepts to provide an integrated solution for internal and external emotion appraisal. We evaluate the performance of the proposed model with different challenging corpora and compare it with state-of-the-art models for external emotion appraisal. To extend the evaluation of the proposed model, we designed and collected a novel dataset based on a Human-Robot Interaction (HRI) scenario. We deployed the model in an iCub robot and evaluated the capability of the robot to learn and describe the affective behavior of different persons based on observation. The performed experiments demonstrate that the proposed model is competitive with the state of the art in describing emotion behavior in general. In addition, it is able to generate internal emotional concepts that evolve through time: it continuously forms and updates the formed emotional concepts, which is a step towards creating an emotional appraisal model grounded in the robot experiences.
The Development of Play

DEFINING PLAY 426 THEORIES OF PLAY 428 Piaget 428 Vygotsky 429 VARIETIES OF PLAY AND THEIR DEVELOPMENTAL COURSE 430 Sensorimotor and Object Play 430 Physical or Locomotor Play 430 Rough-and-Tumble Play 431 Exploratory Play 431 Construction Play 432 Symbolic Play 432  Play and Theory of Mind 433 Symbolic Understanding 439 Object Substitution 441 Distinguishing Pretense From Reality 442 Initiating Pretend Play 446 Does Play Improve Developmental Outcomes? 447 INTERINDIVIDUAL DIFFERENCES IN PLAY 451 Gender Differences in Play 451 The Play of Atypically Developing Children 451 Play Across Cultures 454 FUTURE DIRECTIONS 457 Changing Modes of Play 457 Why Children Pretend 458 Play Across the Life Span 459 CONCLUSION 459
A one-to-many conditional generative adversarial network framework for multiple image-to-image translations

Image-to-Image translation was proposed as a general form of many image learning problems. While generative adversarial networks were successfully applied on many image-to-image translations, many models were limited to specific translation tasks and were difficult to satisfy practical needs. In this work, we introduce a One-to-Many conditional generative adversarial network, which could learn from heterogeneous sources of images. This is achieved by training multiple generators against a discriminator in synthesized learning way. This framework supports generative models to generate images in each source, so output images follow corresponding target patterns. Two implementations, hybrid fake and cascading learning, of the synthesized adversarial training scheme are also proposed, and experimented on two benchmark datasets, UTZap50K and MVOD5K, as well as a new high-quality dataset BehTex7K. We consider five challenging image-to-image translation tasks: edges-to-photo, edges-to-similar-photo translation on UTZap50K, cross-view translation on MVOD5K, and grey-to-color, grey-to-Oil-Paint on BehTex7K. We show that both implementations are able to faithfully translate from an image to another image in edges-to-photo, edges-to-similar-photo, grey-to-color, and grey-to-Oil-Paint translation tasks. The quality of output images in cross-view translation need to be further boosted.
Decision support in intermodal transport: A new research agenda

This paper proposes new research themes concerning decision support in intermodal transport. Decision support models have been constructed for private stakeholders (e.g. network operators, drayage operators, terminal operators or intermodal operators) as well as for public actors such as policy makers and port authorities. Intermodal research topics include policy support, terminal network design, intermodal service network design, intermodal routing, drayage operations and ICT innovations. For each research topic, the current state of the art and gaps in existing models are identified. Current trends in intermodal decision support models include the introduction of environmental concerns, the development of dynamic models and the growth in innovative applications of Operations Research techniques. Limited data availability and problem size (network scale) and related computational considerations are issues which increase the complexity of decision support in intermodal transport.
Positive emotions trigger upward spirals toward emotional well-being.

The broaden-and-build theory of positive emotions predicts that positive emotions broaden the scopes of attention and cognition, and, by consequence, initiate upward spirals toward increasing emotional well-being. The present study assessed this prediction by testing whether positive affect and broad-minded coping reciprocally and prospectively predict one another. One hundred thirty-eight college students completed self-report measures of affect and coping at two assessment periods 5 weeks apart. As hypothesized, regression analyses showed that initial positive affect, but not negative affect, predicted improved broad-minded coping, and initial broad-minded coping predicted increased positive affect, but not reductions in negative affect. Further mediational analyses showed that positive affect and broad-minded coping serially enhanced one another. These findings provide prospective evidence to support the prediction that positive emotions initiate upward spirals toward enhanced emotional wellbeing. Implications for clinical practice and health promotion are discussed.
Online generative model personalization for hand tracking

We present a new algorithm for real-time hand tracking on commodity depth-sensing devices. Our method does not require a user-specific calibration session, but rather learns the geometry as the user performs live in front of the camera, thus enabling seamless virtual interaction at the consumer level. The key novelty in our approach is an online optimization algorithm that jointly estimates pose and shape in each frame, and determines the uncertainty in such estimates. This knowledge allows the algorithm to integrate per-frame estimates over time, and build a personalized geometric model of the captured user. Our approach can easily be integrated in state-of-the-art continuous generative motion tracking software. We provide a detailed evaluation that shows how our approach achieves accurate motion tracking for real-time applications, while significantly simplifying the workflow of accurate hand performance capture. We also provide quantitative evaluation datasets at http://gfx.uvic.ca/datasets/handy
Modeling of a Photovoltaic-Powered Electric Vehicle Charging Station with Vehicle-to-Grid Implementation

This paper is aimed at modelling of a distinct smart charging station for electric vehicles (EVs) that is suitable for DC quick EV charging while ensuring minimum stress on the power grid. Operation of the charging station is managed in such a way that it is either supplied by photovoltaic (PV) power or the power grid, and the vehicle-to-grid (V2G) is also implemented for improving the stability of the grid during peak load hours. The PV interfaced DC/DC converter and grid interfaced DC/AC bidirectional converter share a DC bus. A smooth transition of one operating mode to another demonstrates the effectiveness of the employed control strategy. Modelling and control of the different components are explained and are implemented in Simulink. Simulations illustrate the feasible behaviour of the charging station under all operating modes in terms of the four-way interaction among PV, EVs and the grid along with V2G operation. Additionally, a business model is discussed with comprehensive analysis of cost estimation for the deployment of charging facilities in a residential area. It has been recognized that EVs bring new opportunities in terms of providing regulation services and consumption flexibility by varying the recharging power at a certain time instant. The paper also discusses the potential financial incentives required to inspire EV owners for active participation in the demand response mechanism.
Business: Do you wanna sell more? Discovering Topics, Sentiments and Prediction of Ratings

In the era of Social Computing, the role of customer reviews and ratings can be instrumental in predicting the success and sustainability of businesses as customers and even competitors use them to judge the quality of a business. Yelp is one of the most popular websites for users to write such reviews. This rating can be subjective and biased toward user's personality. Business preferences of a user can be decrypted based on his/ her past reviews. In this paper, we deal with (i) uncovering latent topics in Yelp data based on positive and negative reviews using topic modeling to learn which topics are the most frequent among customer reviews, (ii) sentiment analysis of users' reviews to learn how these topics associate to a positive or negative rating which will help businesses improve their offers and services, and (iii) predicting unbiased ratings from user-generated review text alone, using Linear Regression model. We also perform data analysis to get some deeper insights into customer reviews.
Memory-augmented Dialogue Management for Task-oriented Dialogue Systems

Dialogue management (DM) decides the next action of a dialogue system according to the current dialogue state, and thus plays a central role in task-oriented dialogue systems. Since dialogue management requires to have access to not only local utterances, but also the global semantics of the entire dialogue session, modeling the long-range history information is a critical issue. To this end, we propose a novel MemoryAugmented Dialogue management model (MAD) which employs a memory controller and two additional memory structures, i.e., a slot-value memory and an external memory. The slot-value memory tracks the dialogue state by memorizing and updating the values of semantic slots (for instance, cuisine, price, and location ), and the external memory augments the representation of hidden states of traditional recurrent neural networks through storing more context information. To update the dialogue state efficiently, we also propose slot-level attention on user utterances to extract specific semantic information for each slot. Experiments show that our model can obtain state-of-the-art performance and outperforms existing baselines.
Culture, mind, and the brain: current evidence and future directions.

Current research on culture focuses on independence and interdependence and documents numerous East-West psychological differences, with an increasing emphasis placed on cognitive mediating mechanisms. Lost in this literature is a time-honored idea of culture as a collective process composed of cross-generationally transmitted values and associated behavioral patterns (i.e., practices). A new model of neuro-culture interaction proposed here addresses this conceptual gap by hypothesizing that the brain serves as a crucial site that accumulates effects of cultural experience, insofar as neural connectivity is likely modified through sustained engagement in cultural practices. Thus, culture is "embrained," and moreover, this process requires no cognitive mediation. The model is supported in a review of empirical evidence regarding (a) collective-level factors involved in both production and adoption of cultural values and practices and (b) neural changes that result from engagement in cultural practices. Future directions of research on culture, mind, and the brain are discussed.
Gaussian Processes for Big Data

We introduce stochastic variational inference for Gaussian process models. This enables the application of Gaussian process (GP) models to data sets containing millions of data points. We show how GPs can be variationally decomposed to depend on a set of globally relevant inducing variables which factorize the model in the necessary manner to perform variational inference. Our approach is readily extended to models with non-Gaussian likelihoods and latent variable models based around Gaussian processes. We demonstrate the approach on a simple toy problem and two real world data sets.
Coattention Model for Question Answering

The recently released Stanford Question Answering Dataset (SQuAD) provides a unique version of the question-answer problem that more closely relates to the complex structure of natural language, and thus lends itself to the expressive power of neural networks. We explore combining tested techniques within an encoder-decoder architecture in an attempt to achieve a model that is both accurate and efficient. We ultimately propose a model that utlizes bidirectional LSTM’s fed into a coattention layer, and a fairly simple decoder consisting of an LSTM with two hidden layers. We find through our experimentation that the model performs better than combinations of coattention with both our simpler and more complex decoders. We also find that it excels at answering questions where the answer can rely on marker words or structural context rather than abstract context.
Conditional Neural Processes

Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function. On the other hand, Bayesian methods, such as Gaussian Processes (GPs), exploit prior knowledge to quickly infer the shape of a new function at test time. Yet GPs are computationally expensive, and it can be hard to design appropriate priors. In this paper we propose a family of neural models, Conditional Neural Processes (CNPs), that combine the benefits of both. CNPs are inspired by the flexibility of stochastic processes such as GPs, but are structured as neural networks and trained via gradient descent. CNPs make accurate predictions after observing only a handful of training data points, yet scale to complex functions and large datasets. We demonstrate the performance and versatility of the approach on a range of canonical machine learning tasks, including regression, classification and image completion.
A history of the study of solid tumour growth: the contribution of mathematical modelling.

A miscellany of new strategies, experimental techniques and theoretical approaches are emerging in the ongoing battle against cancer. Nevertheless, as new, ground-breaking discoveries relating to many and diverse areas of cancer research are made, scientists often have recourse to mathematical modelling in order to elucidate and interpret these experimental findings. Indeed, experimentalists and clinicians alike are becoming increasingly aware of the possibilities afforded by mathematical modelling, recognising that current medical techniques and experimental approaches are often unable to distinguish between various possible mechanisms underlying important aspects of tumour development. This short treatise presents a concise history of the study of solid tumour growth, illustrating the development of mathematical approaches from the early decades of the twentieth century to the present time. Most importantly these mathematical investigations are interwoven with the associated experimental work, showing the crucial relationship between experimental and theoretical approaches, which together have moulded our understanding of tumour growth and contributed to current anti-cancer treatments. Thus, a selection of mathematical publications, including the influential theoretical studies by Burton, Greenspan, Liotta et al., McElwain and co-workers, Adam and Maggelakis, and Byrne and co-workers are juxtaposed with the seminal experimental findings of Gray et al. on oxygenation and radio-sensitivity, Folkman on angiogenesis, Dorie et al. on cell migration and a wide variety of other crucial discoveries. In this way the development of this field of research through the interactions of these different approaches is illuminated, demonstrating the origins of our current understanding of the disease.
Personality, attitudes, and intentions: Predicting initial adoption of information security behavior

Investigations of computer user behavior become especially important when behaviors like security software adoption affect organizational information resource security, but adoption antecedents remain elusive. Technology adoption studies typically predict behavioral outcomes by investigating the relationship between attitudes and intentions, though intention may not be the best predictor of actual behavior. Personality constructs have recently been found to explain evenmore variance in behavior, thus providing insights into user behavior. This research incorporates conscientiousness and agreeableness into a conceptual model of security software use. Attitudinal constructs perceived ease of use and perceived usefulness were linked with behavioral intent, while the relationship between intent and actual use was found to be moderated by conscientiousness and agreeableness. The results that the moderating effect of personality greatly increases the amount of variance explained in actual use.
Wheat grass juice reduces transfusion requirement in patients with thalassemia major: a pilot study.

Wheat grass juice is the juice extracted from the pulp of wheat grass and has been used as a general-purpose health tonic for several years. Several of our patients in the thalassemia unit began consuming wheat grass juice after anecdotal accounts of beneficial effects on transfusion requirements. These encouraging experiences prompted us to evaluate the effect of wheat grass juice on transfusion requirements in patients with transfusion dependent beta thalassemia. Families of patients raised the wheat grass at home in kitchen garden/pots. The patients consumed about 100 mL of wheat grass juice daily. Each patient acted as his own control. Observations recorded during the period of intake of wheat grass juice were compared with one-year period preceding it. Variables recorded were the interval between transfusions, pre-transfusion hemoglobin, amount of blood transfused and the body weight. A beneficial effect of wheat grass juice was defined as decrease in the requirement of packed red cells (measured as grams/Kg body weight/year) by 25% or more. 16 cases were analyzed. Blood transfusion requirement fell by >25% in 8 (50%) patients with a decrease of >40% documented in 3 of these. No perceptible adverse effects were recognized.
Practical Bayesian optimization in the presence of outliers

Inference in the presence of outliers is an important field of research as outliers are ubiquitous and may arise across a variety of problems and domains. Bayesian optimization is method that heavily relies on probabilistic inference. This allows outstanding sample efficiency because the probabilistic machinery provides a memory of the whole optimization process. However, that virtue becomes a disadvantage when the memory is populated with outliers, inducing bias in the estimation. In this paper, we present an empirical evaluation of Bayesian optimization methods in the presence of outliers. The empirical evidence shows that Bayesian optimization with robust regression often produces suboptimal results. We then propose a new algorithm which combines robust regression (a Gaussian process with Student-t likelihood) with outlier diagnostics to classify data points as outliers or inliers. By using an scheduler for the classification of outliers, our method is more efficient and has better convergence over the standard robust regression. Furthermore, we show that even in controlled situations with no expected outliers, our method is able to produce better results.