nils-herrmann/clean_scirepeval huggingface dataset:
Comparison of methods: Passing and Bablok regression.

The comparison of methods experiment is important part in process of analytical methods and instruments validation. Passing and Bablok regression analysis is a statistical procedure that allows valuable estimation of analytical methods agreement and possible systematic bias between them. It is robust, non-parametric, non sensitive to distribution of errors and data outliers. Assumptions for proper application of Passing and Bablok regression are continuously distributed data and linear relationship between data measured by two analytical methods. Results are presented with scatter diagram and regression line, and regression equation where intercept represents constant and slope proportional measurement error. Confidence intervals of 95% of intercept and slope explain if their value differ from value zero (intercept) and value one (slope) only by chance, allowing conclusion of method agreement and correction action if necessary. Residual plot revealed outliers and identify possible non-linearity. Furthermore, cumulative sum linearity test is performed to investigate possible significant deviation from linearity between two sets of data. Non linear samples are not suitable for concluding on method agreement.
Reward Model Solution Methods with Impulse and Rate Rewards: An Algorithm and Numerical Results

Reward models have become an important method for specifying performability models for many types of systems. Many methods have been proposed for solving reward models, but no method has proven itself to be applicable over all system classes and sizes. Furthermore, speci cation of reward models has usually been done at the state level, which can be extremely cumbersome for realistic models. We describe a method to specify reward models as stochastic activity networks (SANs) with impulse and rate rewards, and a method by which to solve these models via uniformization. The method is an extension of one proposed by de Souza e Silva and Gail in which impulse and rate rewards are speci ed at the SAN level, and solved in a single model. Furthermore, we propose a new technique for discarding paths in the uniformized process whose contribution to the reward variable is minimal, which greatly reduces the time and space required for a solution. A bound is calculated on the error introduced by this discarding, and its e ectiveness is illustrated through the study of the performability and availability of a degradable multi-processor system.
Association of sleep patterns with psychological positive health and health complaints in children and adolescents

Psychological positive health and health complaints have long been ignored scientifically. Sleep plays a critical role in children and adolescents development. We aimed at studying the association of sleep duration and quality with psychological positive health and health complaints in children and adolescents from southern Spain. A randomly selected two-phase sample of 380 healthy Caucasian children (6–11.9 years) and 304 adolescents (12–17.9 years) participated in the study. Sleep duration (total sleep time), perceived sleep quality (morning tiredness and sleep latency), psychological positive health and health complaints were assessed using the Health Behaviour in School-aged Children questionnaire. The mean (standard deviation [SD]) reported sleep time for children and adolescents was 9.6 (0.6) and 8.8 (0.6) h/day, respectively. Sleep time ≥10 h was significantly associated with an increased likelihood of reporting no health complaints (OR 2.3; P = 0.005) in children, whereas sleep time ≥9 h was significantly associated with an increased likelihood of overall psychological positive health and no health complaints indicators (OR ~ 2; all P < 0.05) in adolescents. Reporting better sleep quality was associated with an increased likelihood of reporting excellent psychological positive health (ORs between 1.5 and 2.6; all P < 0.05). Furthermore, children and adolescents with no difficulty falling asleep were more likely to report no health complaints (OR ~ 3.5; all P < 0.001). Insufficient sleep duration and poor perceived quality of sleep might directly impact quality of life in children, decreasing general levels of psychological positive health and increasing the frequency of having health complaints.
Structuring Legacy Pathology Reports by openEHR Archetypes to Enable Semantic Querying.

Clinical information is often stored as free text, e.g. in discharge summaries or pathology reports. These documents are semi-structured using section headers, numbered lists, items and classification strings. However, it is still challenging to retrieve relevant documents since keyword searches applied on complete unstructured documents result in many false positive retrieval results.



We are concentrating on the processing of pathology reports as an example for unstructured clinical documents. The objective is to transform reports semi-automatically into an information structure that enables an improved access and retrieval of relevant data. The data is expected to be stored in a standardized, structured way to make it accessible for queries that are applied to specific sections of a document (section-sensitive queries) and for information reuse.



Our processing pipeline comprises information modelling, section boundary detection and section-sensitive queries. For enabling a focused search in unstructured data, documents are automatically structured and transformed into a patient information model specified through openEHR archetypes. The resulting XML-based pathology electronic health records (PEHRs) are queried by XQuery and visualized by XSLT in HTML.



Pathology reports (PRs) can be reliably structured into sections by a keyword-based approach. The information modelling using openEHR allows saving time in the modelling process since many archetypes can be reused. The resulting standardized, structured PEHRs allow accessing relevant data by retrieving data matching user queries.



Mapping unstructured reports into a standardized information model is a practical solution for a better access to data. Archetype-based XML enables section-sensitive retrieval and visualisation by well-established XML techniques. Focussing the retrieval to particular sections has the potential of saving retrieval time and improving the accuracy of the retrieval.
An improved TIQ comparator based 3-bit flash ADC

This paper presents an improved TIQ comparator based 3-bit Flash ADC. A modification is suggested for improvement in switching power consumption by eliminating halt stage from 2PASC based TIQ comparator used for ADC. It has been found that switching power consumption is reduced in comparison with other types of FLASH ADC. Switching power dissipation of 27.35pW at 1MHz input signal frequency for 3-bit flash ADC is obtained. The simulation has been carried out at TSMC 180nm Technology in LTspice.
Knowledge Management Process : a theoretical-conceptual research

A gestão do Conhecimento (GC) é um tema que vem despertando o interesse de muitos pesquisadores nas últimas décadas, sendo grande parte das contribuições orientadas por etapas, denominadas processo de GC. Por se tratar de um tema abrangente, as publicações sobre o processo de GC apresentam contribuições multidisciplinares e, desta forma, esta pesquisa tem por objetivo conceituar este processo, analisando as principais abordagens que orientam o estudo de cada etapa, e, também, levantar as principais publicações que tratam do tema, classificando‐as quanto à sua área de contribuição. Para alcançar estes objetivos, este artigo é orientado por uma pesquisa teórico‐conceitual, na qual foram estudados 71 artigos. Os resultados desta pesquisa apontam que o processo de GC é constituído de quatro etapas: aquisição, armazenamento, distribuição e utilização do conhecimento. Na fase de aquisição, as temáticas estudadas são aprendizagem organizacional, absorção de conhecimento, processo criativo e transformação do conhecimento. Na fase de armazenamento, as contribuições tratam do indivíduo, organização e tecnologia da informação, enquanto na fase de distribuição os estudos concentram‐se nas temáticas contato social, comunidade de prática e compartilhamento via tecnologia de informação. E, por fim, na fase de utilização, são abordados os temas forma de utilização, capacidade dinâmica e recuperação e transformação do conhecimento. Palavras-chave: Processo de gestão do conhecimento; Aquisição de conhecimento; Armazenamento de conhecimento; Distribuição de conhecimento; Utilização de conhecimento; Pesquisa teórico‐conceitual.  Knowledge Management (KM) is a subject that has aroused the interest of many researchers in the last decade, being great part of contributions driven by steps, named KM process. Because it is an embracing theme, publications about KM process have multidisciplinary contributions and, thus, this research aims to conceptualize this process, analyzing the main approach that guides the study of each stage, and also, to raise the main publications on the subject, classifying them as to their contribution area. To reach these goals, this article is oriented by a theoretical-conceptual research, in which 71 articles were studied. The results indicate that the KM process consists of four stages: acquisition, storage, distribution, and use of knowledge. In the acquisition phase, the studied themes are organizational learning, knowledge inception, creative process and knowledge transformation. In the storage phase, the contributions deal with a person, an organization and information technology, while in the distribution phase the studies concentrate in social contact themes, practice community and sharing via information technology. And, finally, in the use phase, we address the form of use, dynamic capacity and retrieval and knowledge transformation.
New evolving ensemble classifier for handling concept drifting data streams

Data streams mining have become a novel research topic of growing interest in knowledge discovery. The data streams which are generated from applications, such as network analysis, real time surveillance systems, sensor networks and financial generate huge data streams. These data streams consist of millions or billions of updates and must be processed to extract the useful information. Because of the high speed and huge size of data set in data streams, the traditional classification technologies are no longer applicable. In recent years a great deal of research has been done on this problem, most intends to efficiently solve the data streams mining problem with concept drift. This paper presents a novel approach for data stream classification which handles concept drift. This approach uses weighted majority approach with adaptive sliding window strategies. The experimental result shows that this novel approach works better than other methods.
Learning and exploration in action-perception loops

Discovering the structure underlying observed data is a recurring problem in machine learning with important applications in neuroscience. It is also a primary function of the brain. When data can be actively collected in the context of a closed action-perception loop, behavior becomes a critical determinant of learning efficiency. Psychologists studying exploration and curiosity in humans and animals have long argued that learning itself is a primary motivator of behavior. However, the theoretical basis of learning-driven behavior is not well understood. Previous computational studies of behavior have largely focused on the control problem of maximizing acquisition of rewards and have treated learning the structure of data as a secondary objective. Here, we study exploration in the absence of external reward feedback. Instead, we take the quality of an agent's learned internal model to be the primary objective. In a simple probabilistic framework, we derive a Bayesian estimate for the amount of information about the environment an agent can expect to receive by taking an action, a measure we term the predicted information gain (PIG). We develop exploration strategies that approximately maximize PIG. One strategy based on value-iteration consistently learns faster than previously developed reward-free exploration strategies across a diverse range of environments. Psychologists believe the evolutionary advantage of learning-driven exploration lies in the generalized utility of an accurate internal model. Consistent with this hypothesis, we demonstrate that agents which learn more efficiently during exploration are later better able to accomplish a range of goal-directed tasks. We will conclude by discussing how our work elucidates the explorative behaviors of animals and humans, its relationship to other computational models of behavior, and its potential application to experimental design, such as in closed-loop neurophysiology studies.
DeepProduct: Mobile Product Search With Portable Deep Features

Features extracted by deep networks have been popular in many visual search tasks. This article studies deep network structures and training schemes for mobile visual search. The goal is to learn an effective yet portable feature representation that is suitable for bridging the domain gap between mobile user photos and (mostly) professionally taken product images while keeping the computational cost acceptable for mobile-based applications. The technical contributions are twofold. First, we propose an alternative of the contrastive loss popularly used for training deep Siamese networks, namely robust contrastive loss, where we relax the penalty on some positive and negative pairs to alleviate overfitting. Second, a simple multitask fine-tuning scheme is leveraged to train the network, which not only utilizes knowledge from the provided training photo pairs but also harnesses additional information from the large ImageNet dataset to regularize the fine-tuning process. Extensive experiments on challenging real-world datasets demonstrate that both the robust contrastive loss and the multitask fine-tuning scheme are effective, leading to very promising results with a time cost suitable for mobile product search scenarios.
Comparison of Conservative and Surgical Therapy Concepts for Synechia of the Labia in Pre-Pubertal Girls.

The aim of this study was to evaluate the primary and secondary therapeutic successes of different therapy schemes for the treatment of synechia of the labia in pre-pubertal girls.  The treatment courses of 47 pre-pubertal girls who were treated between February 2007 and February 2013 in the special outpatient clinic for paediatric gynaecology of a department for gynaecology at a German university hospital and for whom information on the course of the disease was available for at least the six months following end of the treatment. 23 of these children were treated with a topical estriol therapy (treatment group A). For 24 of the girls a manual separation of the adhering labia minora was undertaken (treatment group B). Statistical evaluation was performed using the χ2 test, Fischer's exact test and the Mann-Whitney U test.  For 18 of the 23 (80 %) girls in treatment group A topical estriol therapy alone led to a resolution of the synechia. Five of these 23 children (20 %) required a secondary manual separation. All girls for whom treatment was not successful were under 5 years of age. For all 24 girls (100 %) of treatment group B the primary manual separation was performed with success. The recurrence rates after ≥ 6 months in cases with identical after-care did not differ between the two treatment groups (treatment group A: 34 %, treatment group B: 33 %, χ2 test: p = 0.853). 16 of the 17 recurrences occurred ≥ 3 months after the end of the therapy.  Our results show that for children < 5 years of age a 4-week topical therapy with estriol is a promising therapy option for synechia of the labia that is less of a burden for the family situation. Especially for girls ≥ 5 years of age, primary therapy fails in up to 20 % of the cases. Primary manual separation represents a more effective therapeutic option. Irrespective of the treatment applied, a recurrence after ≥ 3 must be expected in one-third of the treated girls.
Improved Scoring Models for Semantic Image Retrieval Using Scene Graphs

Image retrieval via a structured query is explored in Johnson, et al. . The query is structured as a scene graph and a graphical model is generated from the scene graph’s object, attribute, and relationship structure. Inference is performed on the graphical model with candidate images and the energy results are used to rank the best matches. In , scene graph objects that are not in the set of recognized objects are not represented in the graphical model. This work proposes and tests two approaches for modeling the unrecognized objects in order to leverage the attribute and relationship models to improve image retrieval performance.
Vietnamese spelling detection and correction using Bi-gram, Minimum Edit Distance, SoundEx algorithms with some additional heuristics

The spelling checking problem is considered to contain two main phases: the detecting phase and the correcting phase. In this paper, we present a new approach for Vietnamese spelling checking based on Vietnamese characteristics for each phase. Our research approach includes the use of a syllable Bi-gram in combination with parts of speech (POS) to find out suspected syllables. In the correcting phase, we based on minimum edit distance, SoundEx algorithms and some heuristics to build a weight function for assessing suggestion candidates. The training corpus and the test set were collected from e-newspapers.
GABA-modulating phytomedicines for anxiety: A systematic review of preclinical and clinical evidence.

Anxiety disorders are chronic and functionally disabling conditions with high psychological stress, characterised by cognitive symptoms of excessive worry and focus difficulties and physiological symptoms such as muscle tension and insomnia. Gamma-aminobutyric acid (GABA) is an inhibitory neurotransmitter within the central nervous system and is a key target of pharmacotherapies in the treatment of anxiety. Although current pharmaceutical treatments are often efficacious, they may cause undesirable side effects including cognitive decrements and withdrawal symptoms. Plant-based "phytomedicines" may provide novel treatment options, to act as an adjunctive or alternative to existing anxiolytic medications. As such, we conducted a systematic review to assess the current body of literature on anxiolytic phytomedicines and/or phytoconstituents. An open-ended search to 5 July 2017 was conducted using MEDLINE (PubMed), Scopus, and Cochrane library online databases and performed in a stepped format from preclinical to clinical investigations. Eligible studies must have had (a) in vitro evidence of GABA-modulating activity, (b) animal studies using anxiety models to test an anxiolytic effect, and (c) human clinical trials. Ten phytomedicines were identified as having preclinical investigations showing interaction with the GABA system, in addition to human clinical trials: kava, valerian, pennywort, hops, chamomile, Ginkgo biloba, passionflower, ashwagandha, skullcap, and lemon balm. Collectively, the literature reveals preclinical and clinical evidence for various phytomedicines modulating GABA-pathways, with comparative anxiolytic effect to the current array of pharmaceuticals, along with good safety and tolerability profiles.
Learning to Understand Figurative Language : From Similes to Metaphors to Irony

Simile is widely viewed as a less sophisticated conceptual device than metaphor, not least because similes are explicitly marked and are frequently more obvious about the meanings they carry. Nonetheless, this lack of sophistication makes simile an ideal basis for acquiring the category-specific knowledge required to understand metaphor. In this paper we describe a computational approach to simile and metaphor that takes the career-of-metaphor hypothesis of Bowdle and Gentner (2005) as its starting point. We describe how the category-defining knowledge required by metaphor can be acquired from exposure to explicit similes, and demonstrate that this knowledge offers a richer and more diagnostic picture of category structure than that acquired from alternate sources.
Estimation of unnormalized statistical models without numerical integration

Parametric statistical models of continuous or discrete valued data are often not properly normalized, that is, they do not integrate or sum to unity. The normalization is essential for maximum likelihood estimation. While in principle, models can always be normalized by dividing them by their integral or sum (their partition function), this can in practice be extremely difficult. We have been developing methods for the estimation of unnormalized models which do not approximate the partition function using numerical integration. We review these methods, score matching and noise-contrastive estimation, point out extensions and connections both between them and methods by other authors, and discuss their pros and cons.
The Detection and Characterization of Extrasolar Planets

We have now confirmed the existence of > 1800 planets orbiting stars other than the Sun; known as extrasolar planets or exoplanets. The different methods for detecting such planets are sensitive to different regions of parameter space, and so, we are discovering a wide diversity of exoplanets and exoplanetary systems. Characterizing such planets is difficult, but we are starting to be able to determine something of their internal composition and are beginning to be able to probe their atmospheres, the first step towards the detection of bio-signatures and, hence, determining if a planet could be habitable or not. Here, I will review how we detect exoplanets, how we characterize exoplanetary systems and the exoplanets themselves, where we stand with respect to potentially habitable planets and how we are progressing towards being able to actually determine if a planet could host life or not.
Three Levels Load Balancing on Cloudsim

Cloud balancing provides an organization with the ability to distribute application requests across any number of application deployments located in different data centers and through Cloud-computing providers. In this paper, we propose a load balancing methodMinsd (Minimize standard deviation of Cloud load method) and apply it on three levels control: PEs (Processing Elements), Hosts and Data Centers. Simulations on CloudSim are used to check its performance and its influence on makespan, communication overhead and throughput. A true log of a cluster also is used to test our method. Results indicate that our method not only gives good Cloud balancing but also ensures reducing makespan and communication overhead and enhancing throughput of the whole the system.
Evolution of as-a-Service Era in Cloud

Today, a paradigm shift is being observed in science, where the focus is gradually shifting toward the cloud environments to obtain appropriate, robust and affordable services to deal with Big Data challenges (Sharma et al. 2014, 2015a, 2015b). Cloud computing avoids any need to locally maintain the overly scaled computing infrastructure that include not only dedicated space, but the expensive hardware and software also. In this paper, we study the evolution of as-a-Service modalities, stimulated by cloud computing, and explore the most complete inventory of new members beyond traditional cloud computing stack.