nils-herrmann/clean_scirepeval huggingface dataset:
Adolescent idiopathic scoliosis: natural history and long term treatment effects

Adolescent idiopathic scoliosis is a lifetime, probably systemic condition of unknown cause, resulting in a spinal curve or curves of ten degrees or more in about 2.5% of most populations. However, in only about 0.25% does the curve progress to the point that treatment is warranted.Untreated, adolescent idiopathic scoliosis does not increase mortality rate, even though on rare occasions it can progress to the >100 degrees range and cause premature death. The rate of shortness of breath is not increased, although patients with 50 degrees curves at maturity or 80 degrees curves during adulthood are at increased risk of developing shortness of breath. Compared to non-scoliotic controls, most patients with untreated adolescent idiopathic scoliosis function at or near normal levels. They do have increased pain prevalence and may or may not have increased pain severity. Self-image is often decreased. Mental health is usually not affected. Social function, including marriage and childbearing may be affected, but only at the threshold of relatively larger curves.Non-operative treatment consists of bracing for curves of 25 degrees to 35 degrees or 40 degrees in patients with one to two years or more of growth remaining. Curve progression of >/= 6 degrees is 20 to 40% more likely with observation than with bracing. Operative treatment consists of instrumentation and arthrodesis to realign and stabilize the most affected portion of the spine. Lasting curve improvement of approximately 40% is usually achieved.In the most completely studied series to date, at 20 to 28 years follow-up both braced and operated patients had similar, significant, and clinically meaningful reduced function and increased pain compared to non-scoliotic controls. However, their function and pain scores were much closer to normal than patient groups with other, more serious conditions.Risks associated with treatment include temporary decrease in self-image in braced patients. Operated patients face the usual risks of major surgery, a 6 to 29% chance of requiring re-operation, and the remote possibility of developing a pain management problem.Knowledge of adolescent idiopathic scoliosis natural history and long-term treatment effects is and will always remain somewhat incomplete. However, enough is know to provide patients and parents the information needed to make informed decisions about management options.
Cannabis use and risk of psychotic or affective mental health outcomes: a systematic review

Whether cannabis can cause psychotic or affective symptoms that persist beyond transient intoxication is unclear. We systematically reviewed the evidence pertaining to cannabis use and occurrence of psychotic or affective mental health outcomes.



We searched Medline, Embase, CINAHL, PsycINFO, ISI Web of Knowledge,  2006, searched reference lists of studies selected for inclusion, and contacted experts. Studies were included if longitudinal and population based. 35 studies from 4804 references were included. Data extraction and quality assessment were done independently and in duplicate.



There was an increased risk of any psychotic outcome in individuals who had ever used cannabis (pooled adjusted odds ratio=1.41, 95% CI 1.20-1.65). Findings were consistent with a dose-response effect, with greater risk in people who used cannabis most frequently (2.09, 1.54-2.84). Results of analyses restricted to studies of more clinically relevant psychotic disorders were similar. Depression, suicidal thoughts, and anxiety outcomes were examined separately. Findings for these outcomes were less consistent, and fewer attempts were made to address non-causal explanations, than for psychosis. A substantial confounding effect was present for both psychotic and affective outcomes.



The evidence is consistent with the view that cannabis increases risk of psychotic outcomes independently of confounding and transient intoxication effects, although evidence for affective outcomes is less strong. The uncertainty about whether cannabis causes psychosis is unlikely to be resolved by further longitudinal studies such as those reviewed here. However, we conclude that there is now sufficient evidence to warn young people that using cannabis could increase their risk of developing a psychotic illness later in life.
Transgenerational Inheritance of an Acquired Small RNA-Based Antiviral Response in C. elegans

Induced expression of the Flock House virus in the soma of C. elegans results in the RNAi-dependent production of virus-derived, small-interfering RNAs (viRNAs), which in turn silence the viral genome. We show here that the viRNA-mediated viral silencing effect is transmitted in a non-Mendelian manner to many ensuing generations. We show that the viral silencing agents, viRNAs, are transgenerationally transmitted in a template-independent manner and work in trans to silence viral genomes present in animals that are deficient in producing their own viRNAs. These results provide evidence for the transgenerational inheritance of an acquired trait, induced by the exposure of animals to a specific, biologically relevant physiological challenge. The ability to inherit such extragenic information may provide adaptive benefits to an animal.
A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots

Autonomous mobile robots navigating in changing and dynamic unstructured environments like the outdoor environments need to cope with large amounts of uncertainties that are inherent of natural environments. The traditional type-1 fuzzy logic controller (FLC) using precise type-1 fuzzy sets cannot fully handle such uncertainties. A type-2 FLC using type-2 fuzzy sets can handle such uncertainties to produce a better performance. In this paper, we present a novel reactive control architecture for autonomous mobile robots that is based on type-2 FLC to implement the basic navigation behaviors and the coordination between these behaviors to produce a type-2 hierarchical FLC. In our experiments, we implemented this type-2 architecture in different types of mobile robots navigating in indoor and outdoor unstructured and challenging environments. The type-2-based control system dealt with the uncertainties facing mobile robots in unstructured environments and resulted in a very good performance that outperformed the type-1-based control system while achieving a significant rule reduction compared to the type-1 system.
CoChat: Enabling Bot and Human Collaboration for Task Completion

Chatbots have drawn significant attention of late in both industry and academia. For most task completion bots in the industry, human intervention is the only means of avoiding mistakes in complex real-world cases. However, to the best of our knowledge, there is no existing research work modeling the collaboration between task completion bots and human workers. In this paper, we introduce CoChat, a dialog management framework to enable effective collaboration between bots and human workers. In CoChat, human workers can introduce new actions at any time to handle previously unseen cases. We propose a memory-enhanced hierarchical RNN (MemHRNN) to handle the one-shot learning challenges caused by instantly introducing new actions in CoChat. Extensive experiments on real-world datasets well demonstrate that CoChat can relieve most of the human workers’ workload, and get better user satisfaction rates comparing to other state-of-the-art frameworks.
Using artificial intelligence to support compliance with the general data protection regulation

The General Data Protection Regulation (GDPR) is a European Union regulation that will replace the existing Data Protection Directive on 25 May 2018. The most significant change is a huge increase in the maximum fine that can be levied for breaches of the regulation. Yet fewer than half of UK companies are fully aware of GDPR—and a number of those who were preparing for it stopped doing so when the Brexit vote was announced. A last-minute rush to become compliant is therefore expected, and numerous companies are starting to offer advice, checklists and consultancy on how to comply with GDPR. In such an environment, artificial intelligence technologies ought to be able to assist by providing best advice; asking all and only the relevant questions; monitoring activities; and carrying out assessments. The paper considers four areas of GDPR compliance where rule based technologies and/or machine learning techniques may be relevant: Following compliance checklists and codes of conduct; Supporting risk assessments; Complying with the new regulations regarding technologies that perform automatic profiling; Complying with the new regulations concerning recognising and reporting breaches of security. It concludes that AI technology can support each of these four areas. The requirements that GDPR (or organisations that need to comply with GDPR) state for explanation and justification of reasoning imply that rule-based approaches are likely to be more helpful than machine learning approaches. However, there may be good business reasons to take a different approach in some circumstances.
Knowledge tracing with an intelligent agent, in an e-learning platform

E-learning systems have gained nowadays a large student community due to the facility of use and the integration of one-to-one service. Indeed, the personalization of the learning process for every user is needed to increase the student satisfaction and learning efficiency. Nevertheless, the number of students who give up their learning process cannot be neglected. Therefore, it is mandatory to establish an efficient way to assess the level of personalization in such systems. In fact, assessing represents the evolution’s key in every personalized application and especially for the e-learning systems. Besides, when the e-learning system can decipher the student personality, the student learning process will be stabilized, and the dropout rate will be decreased. In this context, we propose to evaluate the personalization process in an e-learning platform using an intelligent referential system based on agents. It evaluates any recommendation made by the e-learning platform based on a comparison. We compare the personalized service of the e-learning system and those provided by our referential system. Therefore, our purpose consists in increasing the efficiency of the proposed system to obtain a significant assessment result; precisely, the aim is to improve the outcomes of every algorithm used in each defined agent. This paper deals with the intelligent agent ‘Mod-Knowledge’ responsible for analyzing the student interaction to trace the student knowledge state. The originality of this agent is that it treats the external and the internal student interactions using machine learning algorithms to obtain a complete view of the student knowledge state. The validation of this contribution is done with experiments showing that the proposed algorithms outperform the existing ones.
IEC 61850 based breaker failure protection scheme design for TNB double busbar substation

IEC 61850 Communication Networks and System for Power Utility Automation is an open standard which enable the information exchange between Intelligent Electronic Devices (IEDs) from different manufacturers. The standard has introduced the standard communication services such as clientserver communication, server-to-server communication named Generic Object Oriented Substation Event (GOOSE)/Generic Substation Status Event (GSSE) message and sampled value communication service for the current and voltage measurement transmission. These communication services provide seamless communication for information exchange between IEDs from different vendors. IEC 61850 has also standardized the engineering process for the Protection and Control (P&C) scheme communication configuration. With the utilization of Ethernet communication technology by IEC 61850 for substation P&C, it can simplify TNB protection and control scheme architecture with less hardwires connection. Based on the advantages of the standard which can be gained by TNB for protection and control scheme, this paper is intended to provide the practical design for IEC 61850 based Breaker Failure Protection (BFP) scheme which can be adopted in TNB's double busbar substation. This paper also specifies the GOOSE message configuration for this scheme design that can be considered for future implementation in TNB IEC 61850 Substation.
Center for the Study of Reading Cognitive Apprenticeship: Teaching the Craft of Reading, Writing, and Mathematics Cognitive Apprenticeship: Teaching the Craft of Reading, Writing, and Mathematics 1. Schooling and Apprenticeship 2. Three Success Models for Cognitive Apprenticeship

Noting that skills and knowledge taught in schools have become abstracted from their uses in the world, this paper clarifies some of the implications for the nature of the knowledge that students acquire through a i;roposal for the retooling of apprenticeship methods for the teaching and learning of cognitive skills. The paper specifically proposes the development of a new cognitive apprenticeship to teach students the thinking and problem-solving skills involved in school subjects such as reading, writing, and mathematics. The first section of the paper, after discussing key shortcomings in current curricular and pedagogical practices, presents some of the structural features of traditional apprenticeship, detailing what would be required to adapt these characteristics to the teaching and learning of cognitive skills. The central section of the paper considers three recently developed pedagogical models that exemplify aspects of apprenticeship methods in teaching thinking and reasoning skills. The section notes that these methods--A. S. Palincsar and A. L. Brown's reciprocal reading teaching, M. Scardamalia and C. Bereiter's procedural facilitation of writing, and A. H. Schoenfeld's method for teaching mathematical problem solving--appear to develop successfully not only the cognitive, but also the metacognitive, skills required for true expertise. The final section organizes ideas on the purposes and characteristics of successful teaching into a general framework for the design of learning "environments," including the content being taught, pedagogical methods employed, sequencing of learning activities, and the sociology of learning--emphasizing how cognitive apprenticeship goes beyond the techniques of traditional apprenticeship. Tables of data are included, and references are appended.
Evaluation of nurse's attitudes toward adult patients of size.

Obesity is a growing epidemic in the United States, with waistlines expanding (overweight) for almost 66% of the population (National Health and Nutrition Examination Survey 1999-2004). The attitude of society, which includes healthcare providers, toward people of size has traditionally been negative, regardless of their own gender, age, experience, and occupation. The purpose of the present study was to determine whether bariatric sensitivity training could improve nursing attitudes and beliefs toward adult obese patients and whether nurses' own body mass index (BMI) affected their attitude and belief scores.



An on-line survey was conducted of nursing attitudes and beliefs regarding adult obese patients. The responses were compared between 1 hospital that offered bariatric sensitivity training and 1 that did not. The primary study measures were 2 scales that have been validated to assess weight bias: Attitudes Toward Obese Persons (ATOP) and Beliefs Against Obese Persons (BAOP). The primary outcome measures were the scores derived from the ATOP and BAOP scales.



Data were obtained from 332 on-line surveys, to which 266 nurses responded with complete data, 145 from hospital 1 (intervention) and 121 from hospital 2 (control). The mean ATOP scores for hospital 1 were modestly greater than those for hospital 2 (18.0 versus 16.1, P = .03). However, no differences were found between the 2 hospitals for the mean BAOP scores (67.1 versus 67.1, P = .86). No statistically significant differences were found between the 2 hospitals among the BMI groups for either ATOP or BAOP. Within each hospital, no statistically significant trend was found among the BMI groups for either ATOP or BAOP. The association of BMI with the overall ATOP (r = .13, P = .04) and BOAP (r = .12, P = .05) scores was very weak, although marginally significant. The association of the overall ATOP score with the BAOP score was weak, although significant (r = .26, P < .001).



Annual bariatric sensitivity training might improve nursing attitudes toward obese patients, but it does not improve nursing beliefs, regardless of the respondent's BMI.
Visual Exploration of Changes in Passenger Flows and Tweets on Mega-City Metro Network

Transportation systems in mega-cities are often affected by various kinds of events such as natural disasters, accidents, and public gatherings. Highly dense and complicated networks in the transportation systems propagate confusion in the network because they offer various possible transfer routes to passengers. Visualization is one of the most important techniques for examining such cascades of unusual situations in the huge networks. This paper proposes visual integration of traffic analysis and social media analysis using two forms of big data: smart card data on the Tokyo Metro and social media data on Twitter. Our system provides multiple coordinated views to visually, intuitively, and simultaneously explore changes in passengers' behavior and abnormal situations extracted from smart card data and situational explanations from real voices of passengers such as complaints about services extracted from social media data. We demonstrate the possibilities and usefulness of our novel visualization environment using a series of real data case studies and domain experts' feedbacks about various kinds of events.
MIMO Broadcasting for Simultaneous Wireless Information and Power Transfer

Wireless power transfer (WPT) is a promising new solution to provide convenient and perpetual energy supplies to wireless networks. In practice, WPT is implementable by various technologies such as inductive coupling, magnetic resonate coupling, and electromagnetic (EM) radiation, for short-/mid-/long-range applications, respectively. In this paper, we consider the EM or radio signal enabled WPT in particular. Since radio signals can carry energy as well as information at the same time, a unified study on simultaneous wireless information and power transfer (SWIPT) is pursued. Specifically, this paper studies a multiple-input multiple-output (MIMO) wireless broadcast system consisting of three nodes, where one receiver harvests energy and another receiver decodes information separately from the signals sent by a common transmitter, and all the transmitter and receivers may be equipped with multiple antennas. Two scenarios are examined, in which the information receiver and energy receiver are separated and see different MIMO channels from the transmitter, or co-located and see the identical MIMO channel from the transmitter. For the case of separated receivers, we derive the optimal transmission strategy to achieve different tradeoffs for maximal information rate versus energy transfer, which are characterized by the boundary of a so-called rate-energy (R-E) region. For the case of co-located receivers, we show an outer bound for the achievable R-E region due to the potential limitation that practical energy harvesting receivers are not yet able to decode information directly. Under this constraint, we investigate two practical designs for the co-located receiver case, namely time switching and power splitting, and characterize their achievable R-E regions in comparison to the outer bound.
A Joint Model for Sentence Semantic Similarity Learning

Sentence similarity is a fundamental task to measure the degree of the likelihood between two sentences. It plays an important role in both NLP and IR communities. The current sentence semantic similarity measurements can be divided into two categories: the model based on feature engineering and the model based on deep learning. These two categories all have their own advantages and disadvantages. The feature captured based on deep learning may not get the deep meaning of the sentence. And the feature exacted based on feature engineering may not be comprehensive. The algorithm proposed recently usually use one of these two models. In this paper, we proposed a new model that combine the deep learning model and the feature engineering model together. Our model enables two methods to complement each other, making the feature extraction more comprehensive: not only take the global semantic information into account, but also further refine the semantic information at the word level. The experiment shows that our model performs better than models only use one category.
Managing technological change in the digital age: the role of architectural frames

Inspired by Herbert Simon’s notion of nearly decomposable systems, researchers have examined modularity as a powerful approach to manage technological change in product innovation. We articulate this approach as the hierarchy-of-parts architecture and explain how it emphasizes decomposition of a design into loosely coupled parts and subsequent aggregation of these into an industrial product. To realize the scale benefits of modularity, firms successively freeze design specifications before production and therefore only allow limited windows of functionality design and redesign. This makes it difficult to take advantage of the increased speed by which digitized products can be developed and modified. To address this problem, we draw on Christopher Alexander’s notion of design patterns to introduce a complementary approach to manage technological change that is resilient to digital technology. We articulate this approach as the network-of-patterns architecture and explain how it emphasizes generalization of ideas into patterns and subsequent specialization of patterns for different design purposes. In response to the increased digitization of industrial products, we demonstrate the value of complementing hierarchy-of-parts thinking with network-of-patterns thinking through a case study of infotainment architecture at an automaker. As a result, we contribute to the literature on managing products in the digital age: we highlight the properties of digital technology that increase the speed by which digitized products can be redesigned; we offer the notion of architectural frames and propose hierarchy-of-parts and network-ofpatterns as frames to support innovation of digitized products; and, we outline an agenda for future research that reconsiders the work of Simon and Alexander as well as their followers to address key challenges in innovating digitized products.  (2014) 29, 27–43. doi:10.1057/jit.2013.30; published online 28 January 2014
A genetic programming approach to designing convolutional neural network architectures

The convolutional neural network (CNN), which is one of the deep learning models, has seen much success in a variety of computer vision tasks. However, designing CNN architectures still requires expert knowledge and a lot of trial and error. In this paper, we attempt to automatically construct CNN architectures for an image classification task based on Cartesian genetic programming (CGP). In our method, we adopt highly functional modules, such as convolutional blocks and tensor concatenation, as the node functions in CGP. The CNN structure and connectivity represented by the CGP encoding method are optimized to maximize the validation accuracy. To evaluate the proposed method, we constructed a CNN architecture for the image classification task with the CIFAR-10 dataset. The experimental result shows that the proposed method can be used to automatically find the competitive CNN architecture compared with state-of-the-art models.