nils-herrmann/clean_scirepeval huggingface dataset:
False-belief understanding in 2.5-year-olds: evidence from two novel verbal spontaneous-response tasks.

Recent research indicates that toddlers and infants succeed at various non-verbal spontaneous-response false-belief tasks; here we asked whether toddlers would also succeed at verbal spontaneous-response false-belief tasks that imposed significant linguistic demands. We tested 2.5-year-olds using two novel tasks: a preferential-looking task in which children listened to a false-belief story while looking at a picture book (with matching and non-matching pictures), and a violation-of-expectation task in which children watched an adult 'Subject' answer (correctly or incorrectly) a standard false-belief question. Positive results were obtained with both tasks, despite their linguistic demands. These results (1) support the distinction between spontaneous- and elicited-response tasks by showing that toddlers succeed at verbal false-belief tasks that do not require them to answer direct questions about agents' false beliefs, (2) reinforce claims of robust continuity in early false-belief understanding as assessed by spontaneous-response tasks, and (3) provide researchers with new experimental tasks for exploring early false-belief understanding in neurotypical and autistic populations.
KDEEP: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks.

Accurately predicting protein-ligand binding affinities is an important problem in computational chemistry since it can substantially accelerate drug discovery for virtual screening and lead optimization. We propose here a fast machine-learning approach for predicting binding affinities using state-of-the-art 3D-convolutional neural networks and compare this approach to other machine-learning and scoring methods using several diverse data sets. The results for the standard PDBbind (v.2016) core test-set are state-of-the-art with a Pearson's correlation coefficient of 0.82 and a RMSE of 1.27 in pK units between experimental and predicted affinity, but accuracy is still very sensitive to the specific protein used. KDEEP is made available via PlayMolecule.org for users to test easily their own protein-ligand complexes, with each prediction taking a fraction of a second. We believe that the speed, performance, and ease of use of KDEEP makes it already an attractive scoring function for modern computational chemistry pipelines.
Adaptability of Neural Networks on Varying Granularity IR Tasks

Recent work in Information Retrieval (IR) using Deep Learning models has yielded state of the art results on a variety of IR tasks. Deep neural networks (DNN) are capable of learning ideal representations of data during the training process, removing the need for independently extracting features. However, the structures of these DNNs are often tailored to perform on specific datasets. In addition, IR tasks deal with text at varying levels of granularity from single factoids to documents containing thousands of words. In this paper, we examine the role of the granularity on the performance of common state of the art DNN structures in IR.
Evolutionary Algorithms for Reinforcement Learning

There are two distinct approaches to solving reinforcement learning problems, namely, searching in value function space and searching in policy space. Temporal di erence methods and evolutionary algorithms are well-known examples of these approaches. Kaelbling, Littman and Moore recently provided an informative survey of temporal di erence methods. This article focuses on the application of evolutionary algorithms to the reinforcement learning problem, emphasizing alternative policy representations, credit assignment methods, and problem-speci c genetic operators. Strengths and weaknesses of the evolutionary approach to reinforcement learning are presented, along with a survey of representative applications.
A balanced scorecard for health services in Afghanistan.

The Ministry of Public Health (MOPH) in Afghanistan has developed a balanced scorecard (BSC) to regularly monitor the progress of its strategy to deliver a basic package of health services. Although frequently used in other health-care settings, this represents the first time that the BSC has been employed in a developing country. The BSC was designed via a collaborative process focusing on translating the vision and mission of the MOPH into 29 core indicators and benchmarks representing six different domains of health services, together with two composite measures of performance. In the absence of a routine health information system, the 2004 BSC for Afghanistan was derived from a stratified random sample of 617 health facilities, 5719 observations of patient-provider interactions, and interviews with 5597 patients, 1553 health workers, and 13,843 households. Nationally, health services were found to be reaching more of the poor than the less-poor population, and providing for more women than men, both key concerns of the government. However, serious deficiencies were found in five domains, and particularly in counselling patients, providing delivery care during childbirth, monitoring tuberculosis treatment, placing staff and equipment, and establishing functional village health councils. The BSC also identified wide variations in performance across provinces; no province performed better than the others across all domains. The innovative adaptation of the BSC in Afghanistan has provided a useful tool to summarize the multidimensional nature of health-services performance, and is enabling managers to benchmark performance and identify strengths and weaknesses in the Afghan context.
IoT Privacy and Security Challenges for Smart Home Environments

Often the Internet of Things (IoT) is considered as a single problem domain, with proposed solutions intended to be applied across a wide range of applications. However, the privacy and security needs of critical engineering infrastructure or sensitive commercial operations are very different to the needs of a domestic Smart Home environment. Additionally, the financial and human resources available to implement security and privacy vary greatly between application domains. In domestic environments, human issues may be as important as technical issues. After surveying existing solutions for enhancing IoT security, the paper identifies key future requirements for trusted Smart Home systems. A gateway architecture is selected as the most appropriate for resource-constrained devices, and for high system availability. Two key technologies to assist system auto-management are identified. Firstly, support for system auto-configuration will enhance system security. Secondly, the automatic update of system software and firmware is needed to maintain ongoing secure system operation.
Plotting basic control charts: tutorial notes for healthcare practitioners.

There is considerable interest in the use of statistical process control (SPC) in healthcare. Although SPC is part of an overall philosophy of continual improvement, the implementation of SPC usually requires the production of control charts. However, as SPC is relatively new to healthcare practitioners and is not routinely featured in medical statistics texts/courses, there is a need to explain the issues involved in the selection and construction of control charts in practice. Following a brief overview of SPC in healthcare and preliminary issues, we use a tutorial-based approach to illustrate the selection and construction of four commonly used control charts (xmr-chart, p-chart, u-chart, c-chart) using examples from healthcare. For each control chart, the raw data, the relevant formulae and their use and interpretation of the final SPC chart are provided together with a notes section highlighting important issues for the SPC practitioner. Some more advanced topics are also mentioned with suggestions for further reading.
A Systematic Mapping Study on Software Engineering Education

Inadequate curriculum for software engineering is considered to be one of the most common software risks. A number of solutions, on improving Software Engineering Education (SEE) have been reported in literature but there is a need to collectively present these solutions at one place. We have performed a mapping study to present a broad view of literature; published on improving the current state of SEE. Our aim is to give academicians, practitioners and researchers an international view of the current state of SEE. Our study has identified 70 primary studies that met our selection criteria, which we further classified and categorized in a well-defined Software Engineering educational framework. We found that the most researched category within the SE educational framework is Innovative Teaching Methods whereas the least amount of research was found in Student Learning and Assessment category. Our future work is to conduct a Systematic Literature Review on SEE. Mapping Study, Software Engineering, Software Engineering Education, Literature Survey.
Text Classification

I use the open-source BBC Dataset, available here: http://mlg.ucd.ie/datasets/bbc.html. The dataset contains 2,225 articles from the BBC news Ibsite corresponding to stories in five topical areas from years 2004-2005. Each article is labeled with one of the following five classes: business, entertainment, politics, sport, and tech. I downloaded the dataset and wrote the script import-data.R to output the data as a two-column data frame, with one row per article. The first column contained the document text, while the second column contained the class labels.
Probability density functions of some skew tent maps

We consider a family of chaotic skew tent maps. The skew tent map is a two-parameter, piecewise-linear, weakly-unimodal, map of the interval Fa;b. We show that Fa;b is Markov for a dense set of parameters in the chaotic region, and we exactly ®nd the probability density function (pdf), for any of these maps. It is well known (Boyarsky A, G ora P. Laws of chaos: invariant measures and dynamical systems in one dimension.  1997), that when a sequence of transformations has a uniform limit F, and the corresponding sequence of invariant pdfs has a weak limit, then that invariant pdf must be F invariant. However, we show in the case of a family of skew tent maps that not only does a suitable sequence of convergent sequence exist, but they can be constructed entirely within the family of skew tent maps. Furthermore, such a sequence can be found amongst the set of Markov transformations, for which pdfs are easily and exactly calculated. We then apply these results to exactly integrate Lyapunov exponents. Ó 2000 Elsevier Science Ltd. All rights reserved.
A Practical Perspective on Latent Structured Prediction for Coreference Resolution

Latent structured prediction theory proposes powerful methods such as Latent Structural SVM (LSSVM), which can potentially be very appealing for coreference resolution (CR). In contrast, only small work is available, mainly targeting the latent structured perceptron (LSP). In this paper, we carried out a practical study comparing for the first time online learning with LSSVM. We analyze the intricacies that may have made initial attempts to use LSSVM fail, i.e., a huge training time and much lower accuracy produced by Kruskal’s spanning tree algorithm. In this respect, we also propose a new effective feature selection approach for improving system efficiency. The results show that LSP, if correctly parameterized, produces the same performance as LSSVM, being at the same time much more efficient.
Sentiment analysis algorithms and applications: A survey

Sentiment analysis; Sentiment classification; Feature selection; Emotion detection; Transfer learning; Building resources Abstract Sentiment Analysis (SA) is an ongoing field of research in text mining field. SA is the computational treatment of opinions, sentiments and subjectivity of text. This survey paper tackles a comprehensive overview of the last update in this field. Many recently proposed algorithms’ enhancements and various SA applications are investigated and presented briefly in this survey. These articles are categorized according to their contributions in the various SA techniques. The related fields to SA (transfer learning, emotion detection, and building resources) that attracted researchers recently are discussed. The main target of this survey is to give nearly full image of SA techniques and the related fields with brief details. The main contributions of this paper include the sophisticated categorizations of a large number of recent articles and the illustration of the recent trend of research in the sentiment analysis and its related areas. 2014 Production and hosting by Elsevier B.V. on behalf of Ain Shams University.
A Deep Cascade Model for Multi-Document Reading Comprehension

A fundamental trade-off between effectiveness and efficiency needs to be balanced when designing an online question answering system. Effectiveness comes from sophisticated functions such as extractive machine reading comprehension (MRC), while efficiency is obtained from improvements in preliminary retrieval components such as candidate document selection and paragraph ranking. Given the complexity of the real-world multi-document MRC scenario, it is difficult to jointly optimize both in an end-to-end system. To address this problem, we develop a novel deep cascade learning model, which progressively evolves from the documentlevel and paragraph-level ranking of candidate texts to more precise answer extraction with machine reading comprehension. Specifically, irrelevant documents and paragraphs are first filtered out with simple functions for efficiency consideration. Then we jointly train three modules on the remaining texts for better tracking the answer: the document extraction, the paragraph extraction and the answer extraction. Experiment results show that the proposed method outperforms the previous state-of-the-art methods on two large-scale multidocument benchmark datasets, i.e., TriviaQA and DuReader. In addition, our online system can stably serve typical scenarios with millions of daily requests in less than 50ms.
A longitudinal study of premorbid IQ Score and risk of developing schizophrenia, bipolar disorder, severe depression, and other nonaffective psychoses.

Longitudinal studies indicate that a lower IQ score increases risk of schizophrenia. Preliminary evidence suggests there is no such effect for nonpsychotic bipolar disorder. To our knowledge, there are no prior population-based, longitudinal studies of premorbid IQ score and risk of developing severe depression requiring hospital admission.



To investigate the association between premorbid IQ score and risk of developing schizophrenia, other nonaffective psychoses, bipolar disorder, and severe depression and to investigate effects of confounding and examine possible causal pathways by which IQ may alter these risks.



Historical cohort study, using record linkage for hospital admissions during a 27-year follow-up period.



Survey of Swedish conscripts (1969-1970).



Population-based sample of 50,087 male subjects. Data were available on IQ score at conscription and on other social and psychological characteristics.



International Classification of Diseases, Eighth Revision or Ninth Revision diagnoses of schizophrenia, bipolar disorder, severe depression, and other nonaffective psychoses.



There was no association between premorbid IQ score and risk of bipolar disorder. Lower IQ was associated with increased risk of schizophrenia, severe depression, and other nonaffective psychoses. Risk of schizophrenia was increased in subjects with average IQ compared with those with high scores, indicating that risk is spread across the whole IQ range.



Lower IQ score was associated with increased risk for schizophrenia, severe depression, and other nonaffective psychoses, but not bipolar disorder. This finding indicates that at least some aspects of the neurodevelopmental etiology of bipolar disorder may differ from these other disorders.
Avoiding the South Side and the Suburbs: The Geography of Mobile Crowdsourcing Markets

Mobile crowdsourcing markets (e.g., Gigwalk and TaskRabbit) offer crowdworkers tasks situated in the physical world (e.g., checking street signs, running household errands). The geographic nature of these tasks distinguishes these markets from online crowdsourcing markets and raises new, fundamental questions. We carried out a controlled study in the Chicago metropolitan area aimed at addressing two key questions: (1) What geographic factors influence whether a crowdworker will be willing to do a task? (2) What geographic factors influence how much compensation a crowdworker will demand in order to do a task? Quantitative modeling shows that travel distance to the location of the task and the socioeconomic status (SES) of the task area are important factors. Qualitative analysis enriches our modeling, with workers mentioning safety and difficulties getting to a location as key considerations. Our results suggest that low-SES areas are currently less able to take advantage of the benefits of mobile crowdsourcing markets. We discuss the implications of our study for these markets, as well as for "sharing economy" phenomena like UberX, which have many properties in common with mobile crowdsourcing markets.
Feature Pyramid Network for Multi-class Land Segmentation

Semantic segmentation is in-demand in satellite imagery processing. Because of the complex environment, automatic categorization and segmentation of land cover is a challenging problem. Solving it can help to overcome many obstacles in urban planning, environmental engineering or natural landscape monitoring. In this paper, we propose an approach for automatic multi-class land segmentation based on a fully convolutional neural network of feature pyramid network (FPN) family. This network is consisted of pre-trained on ImageNet Resnet50 encoder and neatly developed decoder. Based on validation results, leaderboard score and our own experience this network shows reliable results for the DEEPGLOBE - CVPR 2018 land cover classification sub-challenge. Moreover, this network moderately uses memory that allows using GTX 1080 or 1080 TI video cards to perform whole training and makes pretty fast predictions.
Big Data Analytics for Dynamic Energy Management in Smart Grids

The smart electricity grid enables a two-way flow of power and data between suppliers and consumers in order to facilitate the power flow optimization in terms of economic efficiency, reliability and sustainability. This infrastructure permits the consumers and the micro-energy producers to take a more active role in the electricity market and the dynamic energy management (DEM). The most important challenge in a smart grid (SG) is how to take advantage of the users’ participation in order to reduce the cost of power. However, effective DEM depends critically on load and renewable production forecasting. This calls for intelligent methods and solutions for the real-time exploitation of the large volumes of data generated by a vast amount of smart meters. Hence, robust data analytics, high performance computing, efficient data network management, and cloud computing techniques are critical towards the optimized operation of SGs. This research aims to highlight the big data issues and challenges faced by the DEM employed in SG networks. It also provides a brief description of the most commonly used data processing methods in the literature, and proposes a promising direction for future research in the field.
On the Psychology of Prediction

Intuitive predictions follow a judgmental heuristic—representativeness. By this heuristic, people predict the outcome that appears most representative of the evidence. Consequently, intuitive predictions are insensitive to the reliability of the evidence or to the prior probability of the outcome, in violation of the logic of statistical prediction. The hypothesis that people predict by representativeness is supported in a series of studies with both naive and sophisticated subjects. It is shown that the ranking of outcomes by likelihood coincides with their ranking by representativeness and that people erroneously predict rare events and extreme values if these happen to be representative. The experience of unjustified confidence in predictions and the prevalence of fallacious intuitions concerning statistical regression are traced to the representativeness heuristic. In this paper, we explore the rules that determine intuitive predictions and judgments of confidence and contrast these rules to the normative principles of statistical prediction. Two classes of prediction are discussed: category prediction and numerical prediction. In a categorical case, the prediction is given in nominal form, for example, the winner in an election, the diagnosis of a patient, or a person's future occupation. In a numerical case, the prediction is given in numerical form, for example, the future value of a particular stock or of a student's grade point average. In making predictions and judgments under uncertainty, people do not appear to follow the calculus of chance or the statistical theory of prediction. Instead, they rely on a limited number of heuristics which sometimes yield reasonable judgments and sometimes lead to severe and The present paper is concerned with the role of one of these heuristics—representa-tiveness—in intuitive predictions. Given specific evidence (e.g., a personality sketch), the outcomes under consideration (e.g., occupations or levels of achievement) can be ordered by the degree to which they are representative of that evidence. The thesis of this paper is that people predict by representativeness, that is, they select or order outcomes by the 237
Risks and benefits of commonly used herbal medicines in Mexico.

In Mexico, local empirical knowledge about medicinal properties of plants is the basis for their use as home remedies. It is generally accepted by many people in Mexico and elsewhere in the world that beneficial medicinal effects can be obtained by ingesting plant products. In this review, we focus on the potential pharmacologic bases for herbal plant efficacy, but we also raise concerns about the safety of these agents, which have not been fully assessed. Although numerous randomized clinical trials of herbal medicines have been published and systematic reviews and meta-analyses of these studies are available, generalizations about the efficacy and safety of herbal medicines are clearly not possible. Recent publications have also highlighted the unintended consequences of herbal product use, including morbidity and mortality. It has been found that many phytochemicals have pharmacokinetic or pharmacodynamic interactions with drugs. The present review is limited to some herbal medicines that are native or cultivated in Mexico and that have significant use. We discuss the cultural uses, phytochemistry, pharmacological, and toxicological properties of the following plant species: nopal (Opuntia ficus), peppermint (Mentha piperita), chaparral (Larrea divaricata), dandlion (Taraxacum officinale), mullein (Verbascum densiflorum), chamomile (Matricaria recutita), nettle or stinging nettle (Urtica dioica), passionflower (Passiflora incarnata), linden flower (Tilia europea), and aloe (Aloe vera). We conclude that our knowledge of the therapeutic benefits and risks of some herbal medicines used in Mexico is still limited and efforts to elucidate them should be intensified.