nils-herrmann/clean_scirepeval huggingface dataset:
Renewing and Revising SemLink

This research describes SemLink, a comprehensive resource for Natural Language Processing that maps and unifies several highquality lexical resources: PropBank, VerbNet, , and the recently added OntoNotes sense groupings. Each of these resources was created for slightly different purposes, and therefore each carries unique strengths and limitations. SemLink allows users to leverage the strengths of each resource and provides the groundwork for incorporating these lexical resources effectively into linked data resources. SemLink and the resources included therein are discussed with a focus on the value of using lexical resources in a complementary fashion. Recent improvements to SemLink, including the addition of a new resource, the OntoNotes sense groupings, are described. Work to address future goals, including further expansion of SemLink, is also discussed.
A New Compact Microstrip-Fed Dual-Band Coplanar Antenna for WLAN Applications

A novel compact microstrip fed dual-band coplanar antenna for wireless local area network is presented. The antenna comprises of a rectangular center strip and two lateral strips printed on a dielectric substrate and excited using a 50 Omega microstrip transmission line. The antenna generates two separate resonant modes to cover 2.4/5.2/5.8 GHz WLAN bands. Lower resonant mode of the antenna has an impedance bandwidth (2:1 VSWR) of 330 MHz (2190-2520 MHz), which easily covers the required bandwidth of the 2.4 GHz WLAN, and the upper resonant mode has a bandwidth of 1.23 GHz (4849-6070 MHz), covering 5.2/5.8 GHz WLAN bands. The proposed antenna occupy an area of 217 mm2 when printed on FR4 substrate (epsivr=4.7). A rigorous experimental study has been conducted to confirm the characteristics of the antenna. Design equations for the proposed antenna are also developed
Predicting driver drowsiness using vehicle measures: recent insights and future challenges.

Driver drowsiness is a significant contributing factor to road crashes. One approach to tackling this issue is to develop technological countermeasures for detecting driver drowsiness, so that a driver can be warned before a crash occurs.



The goal of this review is to assess, given the current state of knowledge, whether vehicle measures can be used to reliably predict drowsiness in real time.



Several behavioral experiments have shown that drowsiness can have a serious impact on driving performance in controlled, experimental settings. However, most of those studies have investigated simple functions of performance (such as standard deviation of lane position) and results are often reported as averages across drivers, and across time.



Further research is necessary to examine more complex functions, as well as individual differences between drivers.


IMPACT ON INDUSTRY
A successful countermeasure for predicting driver drowsiness will probably require the setting of multiple criteria, and the use of multiple measures.
A fast procedure for computing the distance between complex objects in three-dimensional space

An efficient and reliable algorithm for computing the Euclidean distance between a pair of convex sets in Rm is described. Extensive numerical experience with a broad family of polytopes in R 3 shows that the computational cost is approximately linear in the total number of vertices specifying the two polytopes. The algorithm has special features which makes its application in a variety of robotics problems attractive. These are discussed and an example of collision
Extreme learning machine: Theory and applications

It is clear that the learning speed of feedforward neural networks is in general far slower than required and it has been a major bottleneck in their applications for past decades. Two key reasons behind may be: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms. Unlike these conventional implementations, this paper proposes a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed. The experimental results based on a few artificial and real benchmark function approximation and classification problems including very large complex applications show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms for feedforward neural networks.
The IHS Transformations Based Image Fusion

The IHS sharpening technique is one of the most commonly used techniques for sharpening. Different transformations have been developed to transfer a color image from the RGB space to the IHS space. Through literature, it appears that, various scientists proposed alternative IHS transformations and many papers have reported good results whereas others show bad ones as will as not those obtained which the formula of IHS transformation were used. In addition to that, many papers show different formulas of transformation matrix such as IHS transformation. This leads to confusion what is the exact formula of the IHS transformation?. Therefore, the main purpose of this work is to explore different IHS transformation techniques and experiment it as IHS based image fusion. The image fusion performance was evaluated, in this study, using various methods to estimate the quality and degree of information improvement of a fused image quantitatively.
Gossip Algorithms for Distributed Signal Processing

Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This paper presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.
Determination of difficult intubation in the ED.

The aim of this study is to determine the predictors of difficult intubation in the emergency setting.



This prospective observational clinical study was conducted in the emergency department (ED) of a University Hospital with an annually census of 50 000 visits from May 2005 to May 2007. All patients requiring intubation in the ED were included into the study. During the study period, same airway management protocol was used all intubations. The study form included patient's demographic and variables according to intubation such as the Cormack-Lehane grade, modified LEMON score, Glasgow Coma Scale score, success rate, and associated complications.



A total of 366 patients were included in the study. The mean age of the study patients was 46.8 +/- 22.8, and 68.6% (n = 251) of them were male. A total of 86 (23.5%) patients were classified in the difficult intubation group and 280 (76.5%) patients in easy intubation group. Logistic regression analysis performed by the variables found to be significant in the univariate analysis revealed thyroid-to-hyoid distance less than 2 fingers (odds ratio, 3.34; 95% confidence interval, 1.35-8.27; P = .009) as an independent factor complicating the intubation. Cormack and Lehane classification was strongly related to difficult intubation. Intubation was more difficult from grade 1 to 4 (11% vs 25.2% vs 34% vs 81.8%, respectively; P = .000).



The thyroid-to-hyoid distance less than 2 fingers is the only independent variable in predicting difficult intubation. Mallampati classification is not a useful tool in classifying the difficult intubation in the ED that the "LEMON" acrostic can be modified to "LEON".
Why are some problems hard? Evidence from Tower of Hanoi

This paper analyzes the causes for large differences in difficulty of various isomorphic versions of the Tower of Hanoi problem. Some forms of the problem take 16 times as long to solve, on average, as other versions. Since isomorphism rules out size of task domain as a determinant of relative difficulty, these experiments seek and find causes for the differences in features of the problem representation. Analysis of verbal protocols and the temporal patterning of moves allows the problem-solving behavior to be divided into exploratory and final-path phases. Entry into the final-path phase depends on acquisition of the ability to plan pairs of moves, an achievement made difficult by the working memory load it entails. This memory load can be reduced by automating the rules governing moves, either through problem exploration or training. Once automation has occurred, the solution is obtained very rapidly. Memory load is also proposed as the locus of other differences in difficulty found between various problem rep-
User involvement: a review of the benefits and challenges

Users can be involved in the design process in several ways. The author focuses on user-centred design, participatory design, ethnography, and contextual design. She briefly describes each approach and cites the corresponding literature. The expected benefits of these approaches highly overlap. Methods based on early user involvement promise to avoid the implementation of costly features that users do not want or cannot use. Participatory methods promise to improve the user satisfaction and the acceptance of a system. Nearly all approaches intend to improve the overall usability of the system and by this means increase the productivity and decrease the training costs and the need of user support.
Towards a framework for attribute retrieval

In this paper, we propose an attribute retrieval approach which extracts and ranks attributes from HTML tables. We distinguish between class attribute retrieval and instance attribute retrieval. On one hand, given an instance (e.g. University of Strathclyde) we retrieve from the Web its attributes (e.g. principal, location, number of students). On the other hand, given a class (e.g. universities) represented by a set of instances, we retrieve common attributes of its instances. Furthermore, we show we can reinforce instance attribute retrieval if similar instances are available. Our approach uses HTML tables which are probably the largest source for attribute retrieval. Three recall oriented filters are applied over tables to check the following three properties: (i) is the table relational, (ii) has the table a header, and (iii) the conformity of its attributes and values. Candidate attributes are extracted from tables and ranked with a combination of relevance features. Our approach is shown to have a high recall and a reasonable precision. Moreover, it outperforms state of the art techniques.
A standardized approach to qualitative content analysis of focus group discussions from different countries.

To describe the methodological procedures of a multi-centre focus group research for obtaining content categories also suitable for categorical statistical analyses.



Inductive content analyses were performed on a subsample of 27 focus groups conducted in three different countries, the Netherlands (Utrecht), the UK (Liverpool) and Italy (Verona). The analyses of the subsample of focus group discussions were performed in five steps: (1) independent development of content categories in each of the participating centres, (2) obtaining consensus categories, (3) creation of a manual with coding rules and defining criteria for categories and subcategories, (4) assessment of inter-rater reliability to identify unreliable categories to be revised, and (5) repetition of inter-rater reliability assessment.



The resulting coding system considers five areas: non verbal communication, process oriented expression, task oriented or problem focused expressions, affective or emotional expressions, and physician's personal characteristics. It contains 12 categories of acceptable inter-rater reliability and 41 subcategories.



The coding procedures show how focus group data, obtained in an international multi-centre study can be analysed in a systematic way combining scientific rigour with the richness of data obtainable from qualitative methodologies.



The applied procedures may be helpful for multi-centre focus group research on other topics.
Internet Web servers: workload characterization and performance implications

This paper presents a workload characterization study for Internet Web servers. Six different data sets are used in the study: three from academic environments, two from scientific research organizations, and one from a commercial Internet provider. These data sets represent three different orders of magnitude in server activity, and two different orders of magnitude in time duration, ranging from one week of activity to one year. The workload characterization focuses on the document type distribution, the document size distribution, the document referencing behavior, and the geographic distribution of server requests. Throughout the study, emphasis is placed on finding workload characteristics that are common to all the data sets studied. Ten such characteristics are identified. The paper concludes with a discussion of caching and performance issues, using the observed workload characteristics to suggest performance enhancements that seem promising for Internet Web servers.
Demographic Attributes Prediction on the Real-World Mobile Data

The deluge of the data generated by mobile phone devices imposes new challenges on the data mining community. User activities recorded by mobile phones could be useful for uncovering behavioral patterns. An interesting question is whether patterns in mobile phone usage can reveal demographic characteristics of the user? Demographic information about gender, age, marital status, job type, etc. is a key for applications with customer centric strategies. In this paper, we describe our approach to feature extraction from raw data and building predictive models for the task of demographic attributes predictions. We experimented with graph based representation of users inferred from similarity of their feature vectors, feature selections and classifications algorithms. Our work contributes to the Nokia Mobile Data Challenge (MDC) in the endeavor of exploring the real-world mobile data.
Identifying differentially expressed genes using false discovery rate controlling procedures

DNA microarrays have recently been used for the purpose of monitoring expression levels of thousands of genes simultaneously and identifying those genes that are differentially expressed. The probability that a false identification (type I error) is committed can increase sharply when the number of tested genes gets large. Correlation between the test statistics attributed to gene co-regulation and dependency in the measurement errors of the gene expression levels further complicates the problem. In this paper we address this very large multiplicity problem by adopting the false discovery rate (FDR) controlling approach. In order to address the dependency problem, we present three resampling-based FDR controlling procedures, that account for the test statistics distribution, and compare their performance to that of the naïve application of the linear step-up procedure in Benjamini and Hochberg (1995). The procedures are studied using simulated microarray data, and their performance is examined relative to their ease of implementation.



Comparative simulation analysis shows that all four FDR controlling procedures control the FDR at the desired level, and retain substantially more power then the family-wise error rate controlling procedures. In terms of power, using resampling of the marginal distribution of each test statistics substantially improves the performance over the naïve one. The highest power is achieved, at the expense of a more sophisticated algorithm, by the resampling-based procedures that resample the joint distribution of the test statistics and estimate the level of FDR control.


AVAILABILITY
An R program that adjusts p-values using FDR controlling procedures is freely available over the Internet at www.math.tau.ac.il/~ybenja.
How to detect the Cuckoo Sandbox and to Strengthen it?

Nowadays a lot of malware are analyzed with virtual machines. The Cuckoo sandbox (Cuckoo DevTeam: Cuckoo sandbox. http://www.cuckoosandbox.org , 2013) offers the possibility to log every actions performed by the malware on the virtual machine. To protect themselves and to evande detection, malware need to detect whether they are in an emulated environment or in a real one. With a few modifications and tricks on Cuckoo and the virtual machine we can try to prevent malware to detect that they are under analyze, or at least make it harder. It is not necessary to apply all the modifications, because it may produce a significant overhead and if malware checks his execution time, it may detect an anomaly and consider that it is running in a virtual machine. The present paper will show how a malware can detect the Cuckoo sandbox and how we can counter that.
Online Object Tracking Based on CNN with Metropolis-Hasting Re-Sampling

Tracking-by-learning strategies have been effective in solving many challenging problems in visual tracking, in which the learning sample generation and labeling play important roles for final performance. Since the concern of deep learning based approaches has shown an impressive performance in different vision tasks, how to properly apply the learning model, such as CNN, to an online tracking framework is still challenging. In this paper, to overcome the overfitting problem caused by straight-forward incorporation, we propose an online tracking framework by constructing a CNN based adaptive appearance model to generate more reliable training data over time. With a reformative Metropolis-Hastings re-sampling scheme to reshape particles for a better state posterior representation during online learning, the proposed tracking outperforms most of the state-of-art trackers on challenging benchmark video sequences.
Learning to Ask: Neural Question Generation for Reading Comprehension

We study automatic question generation for sentences from text passages in reading comprehension. We introduce an attention-based sequence learning model for the task and investigate the effect of encoding sentencevs. paragraph-level information. In contrast to all previous work, our model does not rely on hand-crafted rules or a sophisticated NLP pipeline; it is instead trainable end-to-end via sequenceto-sequence learning. Automatic evaluation results show that our system significantly outperforms the state-of-the-art rule-based system. In human evaluations, questions generated by our system are also rated as being more natural (i.e., grammaticality, fluency) and as more difficult to answer (in terms of syntactic and lexical divergence from the original text and reasoning needed to answer).
A randomized, double-blind, placebo-controlled study of the efficacy and safety of vortioxetine 10 mg and 20 mg in adults with major depressive disorder.

Vortioxetine (Lu AA21004) is an antidepressant with a mechanism of action thought to be related to a combination of 2 pharmacologic actions: direct modulation of several receptors and inhibition of the serotonin transporter.



To evaluate the efficacy of vortioxetine 10 and 20 mg once daily in outpatients with major depressive disorder.



This 8-week, multicenter, randomized, double-blind, placebo-controlled, parallel-group study was conducted from July 2010 to January 2012 among adults with a primary diagnosis of recurrent major depressive disorder (DSM-IV-TR).



Eligible subjects were randomized in 1:1:1 ratio to 1 of 3 treatment arms: vortioxetine 10 mg, vortioxetine 20 mg, or placebo once daily for 8 weeks. Subjects who completed the 8-week trial entered a 2-week blinded discontinuation period to assess potential discontinuation symptoms.



The primary endpoint was the least squares mean change in Montgomery-Asberg Depression Rating Scale (MADRS) total score from baseline. Key secondary outcomes were analyzed in the following prespecified sequential order: MADRS response (≥ 50% decrease from baseline in total score), Clinical Global Impressions-Improvement score, change from baseline in MADRS total score in subjects with baseline Hamilton Anxiety Rating Scale score ≥ 20, MADRS remission (total score ≤ 10), and change from baseline in Sheehan Disability Scale total score (all at week 8).



A total of 462 subjects were randomized to placebo (n = 157), vortioxetine 10 mg (n = 155), and vortioxetine 20 mg (n = 150). Mean (SE) reductions from baseline in MADRS total score (week 8) were -10.77 (± 0.807), -12.96 (± 0.832), and -14.41 (± 0.845) for the placebo, vortioxetine 10 mg (P = .058 vs placebo), and vortioxetine 20 mg (P = .002 vs placebo) groups. MADRS response/remission was achieved in 28.4%/14.2%, 33.8%/21.4%, and 39.2%/22.3% of subjects, respectively, in the 3 groups. Only MADRS response for vortioxetine 20 mg significantly separated from placebo (P = .044). Treatment was well tolerated, with the most frequently reported adverse events consisting of nausea, headache, diarrhea, and dizziness.



Vortioxetine 20 mg significantly reduced MADRS total score at 8 weeks in this study population. Overall, vortioxetine was well tolerated in this study.
Approximate Inference with Amortised MCMC

We propose a novel approximate inference algorithm that approximates a target distribution by amortising the dynamics of a user-selected MCMC sampler. The idea is to initialise MCMC using samples from an approximation network, apply the MCMC operator to improve these samples, and finally use the samples to update the approximation network thereby improving its quality. This provides a new generic framework for approximate inference, allowing us to deploy highly complex, or implicitly defined approximation families with intractable densities, including approximations produced by warping a source of randomness through a deep neural network. Experiments consider image modelling with deep generative models as a challenging test for the method. Deep models trained using amortised MCMC are shown to generate realistic looking samples as well as producing diverse imputations for images with regions of missing pixels.