nils-herrmann/clean_scirepeval huggingface dataset:
Status epilepticus: pathophysiology and management in adults

As in Clark and Prout's classic work, we identify three phases of generalised convulsive status epilepticus, which we call impending, established, and subtle. We review physiological and subcellular changes that might play a part in the transition from single seizures to status epilepticus and in the development of time-dependent pharmacoresistance. We review the principles underlying the treatment of status epilepticus and suggest that prehospital treatment is beneficial, that therapeutic drugs should be used in rapid sequence according to a defined protocol, and that refractory status epilepticus should be treated with general anaesthesia. We comment on our preference for drugs with a short elimination half-life and discuss some therapeutic choices.
Design of Diet Recommendation System for Healthcare Service Based on User Information

In the recent aging society, studies on health care services have been actively conducted to provide quality services to medical consumers in wire and wireless environments. However, there are some problems in these health care services due to the lack of personalized service and the uniformed way in services. For solving these issues, studies on customized services in medical markets have been processed. However, because a diet recommendation service is only focused on the personal disease information, it is difficult to provide specific customized services to users. This study provides a customized diet recommendation service for preventing and managing coronary heart disease in health care services. This service provides a customized diet to customers by considering the basic information, vital sign, family history of diseases, food preferences according to seasons and intakes for the customers who are concerning about the coronary heart disease. The users who receive this service can use a customized diet service differed from the conventional service and that supports continuous services and helps changes in customers living habits.
Extending Q-Learning to General Adaptive Multi-Agent Systems

Recent multi-agent extensions of Q-Learning require knowledge of other agents’ payoffs and Q-functions, and assume game-theoretic play at all times by all other agents. This paper proposes a fundamentally different approach, dubbed “Hyper-Q” Learning, in which values of mixed strategies rather than base actions are learned, and in which other agents’ strategies are estimated from observed actions via Bayesian inference. Hyper-Q may be effective against many different types of adaptive agents, even if they are persistently dynamic. Against certain broad categories of adaptation, it is argued that Hyper-Q may converge to exact optimal time-varying policies. In tests using Rock-Paper-Scissors, Hyper-Q learns to significantly exploit an Infinitesimal Gradient Ascent (IGA) player, as well as a Policy Hill Climber (PHC) player. Preliminary analysis of Hyper-Q against itself is also presented.
Digital Image Inpainting : A Review

Image Inpainting is an ancient art and is a research topic in image processing. It fills the corrupted and the lost data in an undetectable manner. Image inpainting finds numerous applications like red eye correction, restoration of old damaged films, compression etc. There are several algorithms proposed for Image Inpainting .This paper reviews the work done by various authors in this field and a comparative study of their techniques. We discuss the different techniques of inpainting like PDE based inpainting, Exemplar based inpainting, bilateral filter inpainting ,fast digital inpainting .The lost information can be reconstructed by propagating the known information in the direction of isophote lines. Isophote lines are the lines of equal intensity. Image Inpainting can be achieved by convolving with the weighted
Privacy-preserving Machine Learning through Data Obfuscation

As machine learning becomes a practice and commodity, numerous cloud-based services and frameworks are provided to help customers develop and deploy machine learning applications. While it is prevalent to outsource model training and serving tasks in the cloud, it is important to protect the privacy of sensitive samples in the training dataset and prevent information leakage to untrusted third parties. Past work have shown that a malicious machine learning service provider or end user can easily extract critical information about the training samples, from the model parameters or even just model outputs. In this paper, we propose a novel and generic methodology to preserve the privacy of training data in machine learning applications. Specifically we introduce a obfuscate function and apply it to the training data before feeding them to the model training task. This function adds random noise to existing samples, or augments the dataset with new samples. By doing so sensitive information about the properties of individual samples, or statistical properties of a group of samples, is hidden. Meanwhile the model trained from the obfuscated dataset can still achieve high accuracy. With this approach, the customers can safely disclose the data or models to third-party providers or end users without the need to worry about data privacy. Our experiments show that this approach can effective defeat four existing types of machine learning privacy attacks at negligible accuracy cost.
Software Platforms for Smart Cities: Concepts, Requirements, Challenges, and a Unified Reference Architecture

Information and communication technologies (ICT) can be instrumental in progressing towards smarter city environments, which improve city services, sustainability, and citizens’ quality of life. Smart City software platforms can support the development and integration of Smart City applications. However, the ICT community must overcome current technological and scientific challenges before these platforms can be widely adopted. This article surveys the state of the art in software platforms for Smart Cities. We analyzed 23 projects concerning the most used enabling technologies, as well as functional and non-functional requirements, classifying them into four categories: Cyber-Physical Systems, Internet of Things, Big Data, and Cloud Computing. Based on these results, we derived a reference architecture to guide the development of next-generation software platforms for Smart Cities. Finally, we enumerated the most frequently cited open research challenges and discussed future opportunities. This survey provides important references to help application developers, city managers, system operators, end-users, and Smart City researchers make project, investment, and research decisions.
Financing renewable energy : who is financing what and why it matters ∗

Accelerating innovation in renewable energy (RE) requires not just more finance, but finance servicing the entire innovation landscape. Given that finance is not ‘neutral’, more information is required on the quality of finance that meets technology and innovation stage-specific financing needs for the commercialization of RE technologies. We investigate the relationship between different financial actors with investment in different RE technologies. We construct a new deal-level dataset of global RE asset finance from 2004 to 2014 based on Bloomberg New Energy Finance data, that distinguishes 10 investor types (e.g. private banks, public banks, utilities) and 11 RE technologies into which they invest. We also construct a heuristic investment risk measure that varies with technology, time and country of investment. We find that particular investor types have preferences for particular risk levels, and hence particular types of RE. Some investor types invested into far riskier portfolios than others, and financing of individual high-risk technologies depended on investment by specific investor types. After the 2008 financial crisis, state-owned or controlled companies and banks emerged as the high-risk taking locomotives of RE asset finance. We use these preliminary results to formulate new questions for future RE policy, and encourage further research.
A Review on Educational Robotics as Assistive Tools For Learning Mathematics and Science

Robots have become more common in our society as it penetrates the education system as well as in industrial area. More researches have been done on robotics and its application in education area. Are the usage of robots in teaching and learning actually work and effective in Malaysian context? What is the importance of educational robotics in education and what skills will be sharpened in using robotics in education? As programming is vital in educational robotics another issues arise – which programming is suitable for Malaysian schools and how to implement it among the students? As per whole discussion, a new robotic curriculum will be suggested. This paper present a review on educational robotics, its advantages to educational fields, the hardware design and the common programming software used which can be implemented among Malaysian students. The results from the overview will help to spark the interest to not only researchers in the field of human–robot interaction but also administration in educational institutes who wish to understand the wider implications of adopting robots in education.
Securing the Internet of Things

This paper presents security of Internet of things. In the Internet of Things vision, every physical object has a virtual component that can produce and consume services Such extreme interconnection will bring unprecedented convenience and economy, but it will also require novel approaches to ensure its safe and ethical use. The Internet and its users are already under continual attack, and a growing economy-replete with business models that undermine the Internet's ethical use-is fully focused on exploiting the current version's foundational weaknesses.
Fast and robust three-dimensional best path phase unwrapping algorithm.

What we believe to be a novel three-dimensional (3D) phase unwrapping algorithm is proposed to unwrap 3D wrapped-phase volumes. It depends on a quality map to unwrap the most reliable voxels first and the least reliable voxels last. The technique follows a discrete unwrapping path to perform the unwrapping process. The performance of this technique was tested on both simulated and real wrapped-phase maps. And it is found to be robust and fast compared with other 3D phase unwrapping algorithms.
A quadratic buck converter with lossless commutation

High switching frequency associated with soft commutation techniques is a new trend in switching converters. Following this trend, the authors present a buck pulsewidth modulation converter, where the dc voltage conversion ratio has a quadratic dependence on duty cycle, providing a large step-down. By introducing two resonant networks, soft switching is attained, providing highly efficient operating conditions for a wide load range at high switching frequency. Contrary to most of the converters that apply soft-switching techniques, the switches presented are not subjected to high switch voltage or current stresses and, consequently, present low conduction losses. The authors present, for this converter, the principle of operation, theoretical analysis, relevant equations, and simulation and experimental results.
Distributed Distributional Deterministic Policy Gradients

This work adopts the very successful distributional perspective on reinforcement learning and adapts it to the continuous control setting. We combine this within a distributed framework for off-policy learning in order to develop what we call the Distributed Distributional Deep Deterministic Policy Gradient algorithm, D4PG. We also combine this technique with a number of additional, simple improvements such as the use of N -step returns and prioritized experience replay. Experimentally we examine the contribution of each of these individual components, and show how they interact, as well as their combined contributions. Our results show that across a wide variety of simple control tasks, difficult manipulation tasks, and a set of hard obstacle-based locomotion tasks the D4PG algorithm achieves state of the art performance.
Test format and corrective feedback modify the effect of testing on long-term retention

We investigated the effects of format of an initial test and whether or not students received corrective feedback on that test on a final test of retention 3 days later. In Experiment 1, subjects studied four short journal papers. Immediately after reading each paper, they received either a multiple choice (MC) test, a short answer (SA) test, a list of statements to read, or a filler task. The MC test, SA test, and list of statements tapped identical facts from the studied material. No feedback was provided during the initial tests. On a final test 3 days later (consisting of MC and SA questions), having had an intervening MC test led to better performance than an intervening SA test, but the intervening MC condition did not differ significantly from the read statements condition. To better equate exposure to test-relevant information, corrective feedback during the initial tests was introduced in Experiment 2. With feedback provided, having had an intervening SA test led to the best performance on the final test, suggesting that the more demanding the retrieval processes engendered by the intervening test, the greater the benefit to final retention. The practical application of these findings is that regular SA quizzes with feedback may be more effective in enhancing student learning than repeated presentation of target facts or taking an MC quiz.
Investigating the Increase in Mobile Phone Evidence in Criminal Activities

The magnification of mobile devices in everyday life prompts the idea that these devices will increasingly have evidential value in criminal cases. While this may have been assumed in digital forensics communities, there has been no empirical evidence to support this idea. This research investigates the extent to which mobile phones are being used in criminal proceedings in the United Kingdom thorough the examination of appeal judgments retrieved from the Westlaw, Lexis Nexis and British and Irish Legal Information Institute (BAILII) legal databases. The research identified 537 relevant appeal cases from a dataset of 12,763 criminal cases referring to mobile phones for a period ranging from 1st of January, 2006 to 31st of July, 2011. The empirical analysis indicates that mobile phone evidence is rising over time with some correlations to particular crimes.
An efficient technique for author name disambiguation

Scholarly digital libraries have become an important source of bibliographic records for scientific communities. Author name search is one of the most common query exercised in digital libraries. The name ambiguity problem in the context of author search in digital libraries, arising from multiple authors sharing the same name, poses many challenges. A number of name disambiguation methods have been proposed in the literature so far. A variety of bibliographic attributes have been considered in these methods. However, hardly any effort has been made to assess the potential contribution of these attributes. We, for the first time, evaluate the potential strength and/or weaknesses of these attributes by a rigorous course of experiments on a large data set. We also explore the potential utility of some attributes from different perspective. A close look reveals that most of the earlier work require one or more attributes which are difficult to obtain in practical applications. Based on this empirical study, we identify three very common and easy to access attributes and propose a two-step hierarchical clustering technique to solve name ambiguity using these attributes only. Experimental results on data set extracted from a popular digital library show that the proposed method achieves significantly high level of accuracy (> 90%) for most of the instances.
The competitive advantage of nations: An application to academia

Within  the  field of bibliometrics,  there  is  sustained  interest  in how nations “compete”  in  terms of academic disciplines, and what determinants explain why countries may have a specific advantage in one discipline over another. However,  this  literature has not,  to date, presented a comprehensive structured model  that could be used  in  the  interpretation of a country’s  research profile and aca‐ demic output. In this paper, we use frameworks from  international business and economics to pre‐ sent such a model. Our study makes four major contributions. First, we include a very wide range of countries and disci‐ plines, explicitly including the Social Sciences, which unfortunately are excluded in most bibliometrics studies.  Second,  we  apply  theories  of  revealed  comparative  advantage  and  the  competitive  ad‐ vantage  of  nations  to  academic  disciplines.  Third, we  cluster  our  34  countries  into  five  different groups that have distinct combinations of revealed comparative advantage  in five major disciplines. Finally, based on our empirical work and prior literature, we present an academic diamond that de‐ tails factors likely to explain a country’s research profile and competitiveness in certain disciplines.
A Research Roadmap of Cognitive Vision

Peter Auer Aude Billard Horst Bischof Isabelle Bloch Pia Boettcher Heinrich Bülthoff Hilary Buxton Henrik Christensen Tony Cohn Patrick Courtney Andrew Crookell James Crowley Sven Dickinson Christof Eberst Jan-Olof Eklundh Bob Fisher Wolfgang Förstner John Gilby Goesta Granlund Vaclav Hlavac Josef Kittler Walter Kropatsch Ales Leonardis Jim Little Giorgio Metta Hans-Hellmut Nagel Bernhard Nebel Bernd Neumann Heinrich Niemann Lucas Paletta Axel Pinz Fiora Pirri Gerhard Sagerer Giulio Sandini Bernt Schiele Rebecca Simpson Gerald Sommer John Tsotsos Monique Thonnat David Vernon Markus Vincze
Evaluation of three high-resolution satellite precipitation estimates : Potential for monsoon monitoring over Pakistan

Multi-sensor precipitation datasets including two products from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and estimates from Climate Prediction Center Morphing Technique (CMORPH) product were quantitatively evaluated to study the monsoon variability over Pakistan. Several statistical and graphical techniques are applied to illustrate the nonconformity of the three satellite products from the gauge observations. During the monsoon season (JAS), the three satellite precipitation products captures the intense precipitation well, all showing high correlation for high rain rates (>30 mm/day). The spatial and temporal satellite rainfall error variability shows a significant geo-topography dependent distribution, as all the three products overestimate over mountain ranges in the north and coastal region in the south parts of Indus basin. The TMPA-RT product tends to overestimate light rain rates (approximately 100%) and the bias is low for high rain rates (about ±20%). In general, daily comparisons from 2005 to 2010 show the best agreement between the TMPA-V7 research product and gauge observations with correlation coefficient values ranging from moderate (0.4) to high (0.8) over the spatial domain of Pakistan. The seasonal variation of rainfall frequency has large biases (100–140%) over high latitudes (36N) with complex terrain for daily, monsoon, and pre-monsoon comparisons. Relatively low uncertainties and errors (Bias ±25% and MAE 1–10 mm) were associated with the TMPA-RT product during the monsoon-dominated region (32–35N), thus demonstrating their potential use for developing an operational hydrological application of the satellite-based near real-time products in Pakistan for flood monitoring. 2014 COSPAR.
Combining Multiple Criteria and Multidimension for Movie Recommender System

Most of current Recommender Systems based on Content-Based Filtering, Collaborative Filtering, Demographic Filtering and Hybrid Filtering which are concentrated on user and item entities. Many research papers are improved by pointing out either Multiple Criteria Rating approach or Multidimensional approach for Recommender System. This paper proposes an advanced Recommender System to provide higher quality of recommendations by combining the Multiple Criteria rating and the Multidimensional approaches. For the Multiple Criteria approach, this paper proposed a method that changes the way of weighting to be more suitable and also concern about the frequency of the selection movie features. To do Multidimensional approach, the Multiple Linear Regression is applied to analyze the contextual information of user characteristics. According to the experimental evaluation, the combining of Multiple Criteria Rating and Multidimensional approaches provide more accurate recommendation results than the current Hybrid Recommender Systems.
Systematic review: cardiovascular safety profile of 5-HT4 agonists developed for gastrointestinal disorders

The nonselective 5-HT(4) receptor agonists, cisapride and tegaserod have been associated with cardiovascular adverse events (AEs).



To perform a systematic review of the safety profile, particularly cardiovascular, of 5-HT(4) agonists developed for gastrointestinal disorders, and a nonsystematic summary of their pharmacology and clinical efficacy.



Articles reporting data on cisapride, clebopride, prucalopride, mosapride, renzapride, tegaserod, TD-5108 (velusetrag) and ATI-7505 (naronapride) were identified through a systematic search of the Cochrane Library, Medline, Embase and Toxfile. Abstracts from UEGW 2006-2008 and DDW 2008-2010 were searched for these drug names, and pharmaceutical companies approached to provide unpublished data.



Retrieved articles on pharmacokinetics, human pharmacodynamics and clinical data with these 5-HT(4) agonists, are reviewed and summarised nonsystematically. Articles relating to cardiac safety and tolerability of these agents, including any relevant case reports, are reported systematically. Two nonselective 5-HT(4) agonists had reports of cardiovascular AEs: cisapride (QT prolongation) and tegaserod (ischaemia). Interactions with, respectively, the hERG cardiac potassium channel and 5-HT(1) receptor subtypes have been suggested to account for these effects. No cardiovascular safety concerns were reported for the newer, selective 5-HT(4) agonists prucalopride, velusetrag, naronapride, or for nonselective 5-HT(4) agonists with no hERG or 5-HT(1) affinity (renzapride, clebopride, mosapride).



5-HT(4) agonists for GI disorders differ in chemical structure and selectivity for 5-HT(4) receptors. Selectivity for 5-HT(4) over non-5-HT(4) receptors may influence the agent's safety and overall risk-benefit profile. Based on available evidence, highly selective 5-HT(4) agonists may offer improved safety to treat patients with impaired GI motility.