nils-herrmann/clean_scirepeval huggingface dataset:
Comparable performance of the Kansas City Cardiomyopathy Questionnaire in patients with heart failure with preserved and reduced ejection fraction.

Despite the growing epidemic of heart failure with preserved ejection fraction (HFpEF), no valid measure of patients' health status (symptoms, function, and quality of life) exists. We evaluated the Kansas City Cardiomyopathy Questionnaire (KCCQ), a validated measure of HF with reduced EF, in patients with HFpEF.



Using a prospective HF registry, we dichotomized patients into HF with reduced EF (EF≤ 40) and HFpEF (EF≥50). The associations between New York Heart Association class, a commonly used criterion standard, and KCCQ Overall Summary and Total Symptom domains were evaluated using Spearman correlations and 2-way ANOVA with differences between patients with HF with reduced EF and HFpEF tested with interaction terms. Predictive validity of the KCCQ Overall Summary scores was assessed with Kaplan-Meier curves for death and all-cause hospitalization. Covariate adjustment was made using Cox proportional hazards models. Internal reliability was assessed with Cronbach's α. Among 849 patients, 200 (24%) had HFpEF. KCCQ summary scores were strongly associated with New York Heart Association class in both patients with HFpEF (r=-0.62; P<0.001) and HF with reduced EF (r=-0.55; P=0.27 for interaction). One-year event-free rates by KCCQ category among patients with HFpEF were 0 to 25=13.8%, 26 to 50=59.1%, 51 to 75=73.8%, and 76 to 100=77.8% (log rank P<0.001), with no significant interaction by EF (P=0.37). The KCCQ domains demonstrated high internal consistency among patients with HFpEF (Cronbach's α=0.96 for overall summary and ≥0.69 in all subdomains).



Among patients with HFpEF, the KCCQ seems to be a valid and reliable measure of health status and offers excellent prognostic ability. Future studies should extend and replicate our findings, including the establishment of its responsiveness to clinical change.
Spectral Sparse Representation for Clustering: Evolved from PCA, K-means, Laplacian Eigenmap, and Ratio Cut

Dimensionality reduction, cluster analysis, and sparse representation are basic components in machine learning. However, their relationships have not yet been fully investigated. In this paper, we find that the spectral graph theory underlies a series of these elementary methods and can unify them into a complete framework. The methods include PCA, K-means, Laplacian eigenmap (LE), ratio cut (Rcut), and a new sparse representation method developed by us, called spectral sparse representation (SSR). Further, extended relations to conventional over-complete sparse representations (e.g., method of optimal directions, KSVD), manifold learning (e.g., kernel PCA, multidimensional scaling, Isomap, locally linear embedding), and subspace clustering (e.g., sparse subspace clustering, low-rank representation) are incorporated. We show that, under an ideal condition from the spectral graph theory, PCA, K-means, LE, and Rcut are unified together. And when the condition is relaxed, the unification evolves to SSR, which lies in the intermediate between PCA/LE and K-mean/Rcut. An efficient algorithm, NSCrt, is developed to solve the sparse codes of SSR. SSR combines merits of both sides: its sparse codes reduce dimensionality of data meanwhile revealing cluster structure. For its inherent relation to cluster analysis, the codes of SSR can be directly used for clustering. Scut, a clustering approach derived from SSR reaches the state-of-the-art performance in the spectral clustering family. The one-shot solution obtained by Scut is comparable to the optimal result of K-means that are run many times. Experiments on various data sets demonstrate the properties and strengths of SSR, NSCrt, and Scut.
Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment

Multimodal affective computing, learning to recognize and interpret human affect and subjective information from multiple data sources, is still challenging because:(i) it is hard to extract informative features to represent human affects from heterogeneous inputs; (ii) current fusion strategies only fuse different modalities at abstract levels, ignoring time-dependent interactions between modalities. Addressing such issues, we introduce a hierarchical multimodal architecture with attention and word-level fusion to classify utterance-level sentiment and emotion from text and audio data. Our introduced model outperforms state-of-the-art approaches on published datasets, and we demonstrate that our model's synchronized attention over modalities offers visual interpretability.
ZombieCoin: Powering Next-Generation Botnets with Bitcoin

Botnets are the preeminent source of online crime and arguably the greatest threat to the Internet infrastructure. In this paper, we present ZombieCoin, a botnet command-and-control (C&C) mechanism that runs on the Bitcoin network. ZombieCoin offers considerable advantages over existing C&C techniques, most notably the fact that Bitcoin is designed to resist the very regulatory processes currently used to combat botnets. We believe this is a desirable avenue botmasters may explore in the near future and our work is intended as a first step towards devising effective countermeasures.
Adding Data Mining Support to SPARQL Via Statistical Relational Learning Methods

Exploiting the complex structure of relational data enables to build better models by taking into account the additional information provided by the links between objects. We extend this idea to the Semantic Web by introducing our novel SPARQL-ML approach to perform data mining for Semantic Web data. Our approach is based on traditional SPARQL and statistical relational learning methods, such as Relational Probability Trees and Relational Bayesian Classifiers. We analyze our approach thoroughly conducting three sets of experiments on synthetic as well as real-world data sets. Our analytical results show that our approach can be used for any Semantic Web data set to perform instance-based learning and classification. A comparison to kernel methods used in Support Vector Machines shows that our approach is superior in terms of classification accuracy. Adding Data Mining Support to SPARQL via Statistical Relational Learning Methods Christoph Kiefer, Abraham Bernstein, and André Locher Department of Informatics, University of Zurich, Switzerland {kiefer,bernstein}@ifi.uzh.ch,  Exploiting the complex structure of relational data enables to build better models by taking into account the additional information provided by the links between objects. We extend this idea to the Semantic Web by introducing our novel SPARQL-ML approach to perform data mining for Semantic Web data. Our approach is based on traditional SPARQL and statistical relational learning methods, such as Relational Probability Trees and Relational Bayesian Classifiers. We analyze our approach thoroughly conducting three sets of experiments on synthetic as well as real-world data sets. Our analytical results show that our approach can be used for any Semantic Web data set to perform instance-based learning and classification. A comparison to kernel methods used in Support Vector Machines shows that our approach is superior in terms of classification accuracy. Exploiting the complex structure of relational data enables to build better models by taking into account the additional information provided by the links between objects. We extend this idea to the Semantic Web by introducing our novel SPARQL-ML approach to perform data mining for Semantic Web data. Our approach is based on traditional SPARQL and statistical relational learning methods, such as Relational Probability Trees and Relational Bayesian Classifiers. We analyze our approach thoroughly conducting three sets of experiments on synthetic as well as real-world data sets. Our analytical results show that our approach can be used for any Semantic Web data set to perform instance-based learning and classification. A comparison to kernel methods used in Support Vector Machines shows that our approach is superior in terms of classification accuracy.
Educational Data Mining Tools: A Survey from 2001 to 2016

Educational data mining (EDM) is an up-coming interdisciplinary research field, in which data mining (DM) techniques applying in educational data. Its objective is to better understand how students gain knowledge and recognize the settings in which they learn to improve educational outcomes. Educational systems can store a huge amount of data that coming from multiple sources in different formats. Each particular educational problem requires different types of the mining techniques because traditional DM techniques cannot be applied directly to these types of data and problems. There are many general data mining tools are available but these are not designed for deal with educational data and an educator cannot use these tools without knowledge of data mining concepts. To overcome from this problem many authors provided educational data mining tools with different data mining techniques for different purposes. This paper surveys the EDM Tools from 2001 to 2016 and presents the consolidated list of tools in one place to help the EDM researchers. Then categorizes the tools based on data mining methods and explains the usage of EDM Tools.
Indirect measures of gene flow and migration: FST≠1/(4Nm+1)

The difficulty of directly measuring gene flow has lead to the common use of indirect measures extrapolated from genetic frequency data. These measures are variants of FST, a standardized measure of the genetic variance among populations, and are used to solve for Nm, the number of migrants successfully entering a population per generation. Unfortunately, the mathematical model underlying this translation makes many biologically unrealistic assumptions; real populations are very likely to violate these assumptions, such that there is often limited quantitative information to be gained about dispersal from using gene frequency data. While studies of genetic structure per se are often worthwhile, and FST is an excellent measure of the extent of this population structure, it is rare that FST can be translated into an accurate estimate of Nm.
Folliculocentric Lichen Sclerosus et Atrophicus in a 10-Year-Old Girl.

Lichen sclerosus et atrophicus (LSA) is a chronic inflammatory scarring disease with a predilection for the anogenital area; however, 15%-20% of LSA cases are extragenital. The folliculocentric variant is rarely reported and less well understood. The authors report a rare case of extragenital, folliculocentric LSA in a 10-year-old girl. The patient presented to the dermatology clinic for evaluation of an asymptomatic eruption of the arms and legs, with no vaginal or vulvar involvement. Physical examination revealed the presence of numerous 2-4 mm, mostly perifollicular, hypopigmented, slightly atrophic papules and plaques. Many of the lesions had a central keratotic plug. Cutaneous histopathological examination showed features of LSA. Based on clinical and histological findings, folliculocentric extragenital LSA was diagnosed.
Measuring reactive species and oxidative damage in vivo and in cell culture: how should you do it and what do the results mean?

Free radicals and other reactive species (RS) are thought to play an important role in many human diseases. Establishing their precise role requires the ability to measure them and the oxidative damage that they cause. This article first reviews what is meant by the terms free radical, RS, antioxidant, oxidative damage and oxidative stress. It then critically examines methods used to trap RS, including spin trapping and aromatic hydroxylation, with a particular emphasis on those methods applicable to human studies. Methods used to measure oxidative damage to DNA, lipids and proteins and methods used to detect RS in cell culture, especially the various fluorescent "probes" of RS, are also critically reviewed. The emphasis throughout is on the caution that is needed in applying these methods in view of possible errors and artifacts in interpreting the results.
Laser grooving of semiconductor wafers: Comparing a simplified numerical approach with experiments

Laser grooving is used for the singulation of advanced CMOS wafers since it is believed that it exerts lower mechanical stress than traditional blade dicing. The very local heating of wafers, however, might result in high thermal stress around the heat affected zone. In this work we present a model to predict the temperature distribution, material removal, and the resulting stress, in a sandwiched structure of metals and dielectric materials that are commonly found in the back-end of line of semiconductor wafers. Simulation results on realistic three dimensional back-end structures reveal that the presence of metals clearly affects both the ablation depth, and the stress in the material. Experiments showed a similar observation for the ablation depth. The shape of the crater, however, was found to be more uniform than predicted by simulations, which is probably due to the redistribution of molten metal.
Plan choice, health insurance cost and premium sharing.

We develop a model of premium sharing for firms that offer multiple insurance plans. We assume that firms offer one low quality plan and one high quality plan. Under the assumption of wage rigidities we found that the employee's contribution to each plan is an increasing function of that plan's premium. The effect of the other plan's premium is ambiguous. We test our hypothesis using data from the Employer Health Benefit Survey. Restricting the analysis to firms that offer both HMO and PPO plans, we measure the amount of the premium passed on to employees in response to a change in both premiums. We find evidence of large and positive effects of the increase in the plan's premium on the amount of the premium passed on to employees. The effect of the alternative plan's premium is negative but statistically significant only for the PPO plans.
Towards Large-Scale, Heterogeneous Anomaly Detection Systems in Industrial Networks: A Survey of Current Trends

Industrial Networks (INs) are widespread environments where heterogeneous devices collaborate to control and monitor physical processes. Some of the controlled processes belong to Critical Infrastructures (CIs), and, as such, IN protection is an active research field. Among different types of security solutions, IN Anomaly Detection Systems (ADSs) have received wide attention from the scientific community. While INs have grown in size and in complexity, requiring the development of novel, Big Data solutions for data processing, INADSs have not evolved at the same pace. In parallel, the development of BigData frameworks such asHadoop or Spark has led the way for applying Big Data Analytics to the field of cyber-security, mainly focusing on the Information Technology (IT) domain. However, due to the particularities of INs, it is not feasible to directly apply IT security mechanisms in INs, as IN ADSs face unique characteristics. In this work we introduce three main contributions. First, we survey the area of Big Data ADSs that could be applicable to INs and compare the surveyed works. Second, we develop a novel taxonomy to classify existing INbased ADSs. And, finally, we present a discussion of open problems in the field of Big Data ADSs for INs that can lead to further development.
CNN-Based Joint Clustering and Representation Learning with Feature Drift Compensation for Large-Scale Image Data

Given a large unlabeled set of images how to efficiently and effectively group them into clusters based on extracted visual representations remains a challenging problem. To address this problem we propose a convolutional neural network (CNN) to jointly solve clustering and representation learning in an iterative manner. In the proposed method given an input image set we first randomly pick k samples and extract their features as initial cluster centroids using the proposed CNN with an initial model pretrained from the ImageNet dataset. Mini-batch k-means is then performed to assign cluster labels to individual input samples for a mini-batch of images randomly sampled from the input image set until all images are processed. Subsequently the proposed CNN simultaneously updates the parameters of the proposed CNN and the centroids of image clusters iteratively based on stochastic gradient descent. We also propose a feature drift compensation scheme to mitigate the drift error caused by feature mismatch in representation learning. Experimental results demonstrate the proposed method outperforms start-of-the-art clustering schemes in terms of accuracy and storage complexity on large-scale image sets containing millions of images.
Assessment of existing offshore structures for life extension

In decision making under uncertainty there are two main questions that need to be evaluated: i) What are the future consequences and associated uncertainties of an action, and ii) what is a good (or right) decision or action. Philosophically these issues are categorised as epistemic questions (i.e. questions of knowledge) and ethical questions (i.e. questions of moral and norms). This paper discusses the second issue, and evaluates different bases for a good decision, using different ethical theories as a starting point. This includes the utilitarian ethics of Bentley and Mills, and deontological ethics of Kant, Rawls and Habermas. The paper addresses various principles in risk management and risk related decision making, including cost benefit analysis, minimum safety criterion, the ALARP principle and the precautionary principle.
A Visual Analysis Approach for Community Detection of Multi-Context Mobile Social Networks

The problem of detecting community structures of a social network has been extensively studied over recent years, but most existing methods solely rely on the network structure and neglect the context information of the social relations. The main reason is that a context-rich network offers too much flexibility and complexity for automatic or manual modulation of the multifaceted context in the analysis process. We address the challenging problem of incorporating context information into the community analysis with a novel visual analysis mechanism. Our approach consists of two stages: interactive discovery of salient context, and iterative context-guided community detection. Central to the analysis process is a context relevance model (CRM) that visually characterizes the influence of a given set of contexts on the variation of the detected communities, and discloses the community structure in specific context configurations. The extracted relevance is used to drive an iterative visual reasoning process, in which the community structures are progressively discovered. We introduce a suite of visual representations to encode the community structures, the context as well as the CRM. In particular, we propose an enhanced parallel coordinates representation to depict the context and community structures, which allows for interactive data exploration and community investigation. Case studies on several datasets demonstrate the efficiency and accuracy of our approach.
ALKALINE PRETREATMENT OF SPRUCE AND BIRCH TO IMPROVE BIOETHANOL AND BIOGAS PRODUCTION

Alkaline pretreatment with NaOH under mild operating conditions was used to improve ethanol and biogas production from softwood spruce and hardwood birch. The pretreatments were carried out at different temperatures between minus 15 and 100oC with 7.0% w/w NaOH solution for 2 h. The pretreated materials were then enzymatically hydrolyzed and subsequently fermented to ethanol or anaerobically digested to biogas. In general, the pretreatment was more successful for both ethanol and biogas production from the hardwood birch than the softwood spruce. The pretreatment resulted in significant reduction of hemicellulose and the crystallinity of cellulose, which might be responsible for improved enzymatic hydrolyses of birch from 6.9% to 82.3% and spruce from 14.1% to 35.7%. These results were obtained with pretreatment at 100°C for birch and 5°C for spruce. Subsequently, the best ethanol yield obtained was 0.08 g/g of the spruce while pretreated at 100°C, and 0.17 g/g of the birch treated at 100°C. On the other hand, digestion of untreated birch and spruce resulted in methane yields of 250 and 30 l/kg VS of the wood species, respectively. The pretreatment of the wood species at the best conditions for enzymatic hydrolysis resulted in 83% and 74% improvement in methane production from birch and spruce.
Maximum daily energy intake: it takes time to lift the metabolic ceiling.

Conventionally, maximum capacities for energy assimilation are presented as daily averages. However, maximum daily energy intake is determined by the maximum metabolizable energy intake rate and the time available for assimilation of food energy. Thrush nightingales (Luscinia luscinia) in migratory disposition were given limited food rations for 3 d to reduce their energy stores. Subsequently, groups of birds were fed ad lib. during fixed time periods varying between 7 and 23 h per day. Metabolizable energy intake rate, averaged over the available feeding time, was 1.9 W and showed no difference between groups on the first day of refueling. Total daily metabolizable energy intake increased linearly with available feeding time, and for the 23-h group, it was well above suggested maximum levels for animals. We conclude that both intake rate and available feeding time must be taken into account when interpreting potential constraints acting on animals' energy budgets. In the 7-h group, energy intake rates increased from 1.9 W on the first day to 3.1 W on the seventh day. This supports the idea that small birds can adaptively increase their energy intake rates on a short timescale.
Mining Scientific Papers for Bibliometrics: A (Very) Brief Survey of Methods and Tools

The Open Access movement in scientific publishing and search engines like Google Scholar have made scientific articles more broadly accessible. During the last decade, the availability of scientific papers in full text has become more and more widespread thanks to the growing number of publications on online platforms such as ArXiv and CiteSeer (Wu, 2014). The efforts to provide articles in machine-readable formats and the rise of Open Access publishing have resulted in a number of standardized formats for scientific papers (such as NLM-JATS, TEI, DocBook).
Face alignment with Cascaded Bidirectional LSTM Neural Networks

Face alignment is an important issue in many computer vision problems. The key problem is to find the nonlinear mapping from face image or feature to landmark locations. In this paper, we propose a novel cascaded approach with bidirectional Long Short Term Memory (LSTM) neural networks to approximate this nonlinear mapping. The cascaded structure is used to reduce the complexity of this problem and accelerate the algorithm by conducting the coarse-to-fine search. In each cascaded module, features of landmarks are delivered as inputs into the bidirectional LSTM network. The depth of the network guarantees the ability to learn highly complex mapping. The recurrent connections in LSTM explore the relationships of different landmarks and ensure that the shape of the face is maintained. On several challenging public databases, our approach achieves state-of-the-art performances.