nils-herrmann/clean_scirepeval huggingface dataset:
The Impact of Server Virtualization on ITIL Processes

Server virtualization influences all aspects of IT service management, and is a key enabling technology for cloud computing. In this paper we focus on the impact of server virtualization on service delivery and service support as described by ITIL. We identify advantages, disadvantages, and risks of server virtualization for capacity, management, availability, costs, and security of IT services, and relate these aspects to the ITIL processes. We validated our results using an empirical test within four different organizations. Our main conclusion is that server virtualization does not change the ITIL processes themselves, but it does change the way the processes are executed. Server virtualization is no silver bullet for solving problems in IT operations and management. If server virtualization has been properly introduced, it can offer faster and better execution of the ITIL processes. The impact is most significant on the Financial Management process, while also Service Level Management, Incident Management, Change Management, IT Service Continuity Management and Availability Management are affected considerably. The impact is less prominent for Application Management, Software Asset Management, Release Management, Configuration Management and Security Management.
A high performance CMOS direct down conversion mixer for UWB system

This paper represents a high performance wideband CMOS direct down-conversion mixer for UWB based on 0.18 μm CMOS technology. The proposed mixer uses the current bleeding technique and an extra resonant inductor to improve the conversion gain, noise figure (NF) and linearity. Also, with an extra inductor and the careful choosing of transistor sizes, the mixer has a very low flicker noise. The shunt resistor matching is applied to have a 528MHz bandwidth matching at 50 Ohm. The simulation results show the voltage conversion gain of 20.5 dB, the double-side band NF of 5.6 dB. Two-tone test result indicates 11.25 dBm of IIP3 and higher than 70 dBm of IIP2. The circuit operates at the supply voltage of 1.8 V, and dissipates 11.5 mW.
Evidence-based decision making in action: Part 1--Finding the best clinical evidence.

The purpose of this article is to introduce evidence-based concepts and demonstrate how to find valid evidence to answer clinical questions. Evidence-based decision making (EBDM) requires understanding new concepts and developing new skills including how to: ask good clinical questions, conduct a computerized search, critically appraise the evidence, apply the results in clinical practice, and evaluate the process. This approach recognizes that clinicians can never be completely current with all conditions, medications, materials, or available products. Thus EBDM provides a mechanism for addressing these gaps in knowledge in order to provide the best care possible. In Part 1, a case scenario demonstrates the application of the skills involved in structuring a clinical question and conducting an online search using PubMed. Practice tips are provided along with online resources related to the evidence-based process.
IMPROVING TLS SECURITY BY QUANTUM CRYPTOGRAPHY

Quantum Cryptography or Quantum Key Distribution (QKD) solves the key distribution problem by allowing the exchange of a cryptographic key between two remote parties with absolute security, guaranteed by the laws of quantum physics. Extensive studies have been undertaken on QKD since it was noted that quantum computers could break public key cryptosystems based on number theory. Actually, the progress of research in this field allows the implementation of QKD outside of laboratories. Efforts are made to exploit this technology in the existing communication networks and to improve the performance and reliability of the implemented technologies. Some research is in progress for the integration of QKD with the protocols in different layers of OSI model. The examples of such research effort are the integration of QKD in point-to-point protocol (PPP) OSI layer 2 and the integration of QKD with IPSEC at OSI layer-3. All these works are moving towards the utilization of QKD technology for enhancing the security of modern computing applications on the internet. In this paper, we present a novel extension of the TLS protocol based on QKD. We introduce a scheme for integrating Quantum Cryptography in this protocol. Our approach improves the security of the process of authentication and data encryption. Also, we describe an example to illustrate the feasibility of our scheme’s implementation.
Learning similarity metrics for event identification in social media

Social media sites (e.g., Flickr, YouTube, and Facebook) are a popular distribution outlet for users looking to share their experiences and interests on the Web. These sites host substantial amounts of user-contributed materials (e.g., photographs, videos, and textual content) for a wide variety of real-world events of different type and scale. By automatically identifying these events and their associated user-contributed social media documents, which is the focus of this paper, we can enable event browsing and search in state-of-the-art search engines. To address this problem, we exploit the rich "context" associated with social media content, including user-provided annotations (e.g., title, tags) and automatically generated information (e.g., content creation time). Using this rich context, which includes both textual and non-textual features, we can define appropriate document similarity metrics to enable online clustering of media to events. As a key contribution of this paper, we explore a variety of techniques for learning multi-feature similarity metrics for social media documents in a principled manner. We evaluate our techniques on large-scale, real-world datasets of event images from Flickr. Our evaluation results suggest that our approach identifies events, and their associated social media documents, more effectively than the state-of-the-art strategies on which we build.
Does Economic Growth Reduce Corruption ? Theory and Evidence from Vietnam ∗

Government corruption is more prevalent in poor countries than in rich countries. This paper uses cross-industry heterogeneity in growth rates within Vietnam to test empirically whether growth leads to lower corruption. We find that it does. We begin by developing a model of government officials’ choice of how much bribe money to extract from firms that is based on the notion of inter-regional tax competition, and consider how officials’ choices change as the economy grows. We show that economic growth is predicted to decrease the rate of bribe extraction under plausible assumptions, with the benefit to officials of demanding a given share of revenue as bribes outweighed by the increased risk that firms will move elsewhere. This effect is dampened if firms are less mobile. Our empirical analysis uses survey data collected from over 13,000 Vietnamese firms between 2006 and 2010 and an instrumental variables strategy based on industry growth in other provinces. We find, first, that firm growth indeed causes a decrease in bribe extraction. Second, this pattern is particularly true for firms with strong land rights and those with operations in multiple provinces, consistent with these firms being more mobile. Our results suggest that as poor countries grow, corruption could subside “on its own,” and they demonstrate one type of positive feedback between economic growth and good institutions.  We thank Lori Beaman, Raymond Fisman, Chang-Tai Hsieh, Supreet Kaur, Neil McCulloch, Andrei Shleifer, Matthew Stephenson, Eric Verhoogen, and Ekaterina Zhuravskaya for helpful comments.
Methodological framework for data processing based on the Data Science paradigm

This paper describes the steps for achieving data processing in a methodological context, which take part of a methodology previously proposed by the authors for developing Data Mining (DM) applications, called "Methodology for the development of data mining applications based on organizational analysis". The methodology has three main phases: Knowledge of the Organization, Preparation and treatment of data, and finally, development of the DM application. We will focus on the second phase. The main contribution of this proposal is the design of a methodological framework of the second phase based on the paradigm of Data Science (DS), in order to get what we have called “Vista Minable Operacional” (VMO) from the “Vista Minable Conceptual” (VMC). The VMO built is used in the third phase. This methodological framework has been applied in two different cases of study, oil and public health.
Attendance dynamics at work: the antecedents and correlates of presenteeism, absenteeism, and productivity loss.

Presenteeism is attending work when ill. This study examined the antecedents and correlates of presenteeism, absenteeism, and productivity loss attributed to presenteeism. Predictors included work context, personal characteristics, and work experiences. Business school graduates employed in a variety of work positions (N = 444) completed a Web-based survey. Presenteeism was positively associated with task significance, task interdependence, ease of replacement, and work to family conflict and negatively associated with neuroticism, equity, job security, internal health locus of control, and the perceived legitimacy of absence. Absenteeism was positively related to task significance, perceived absence legitimacy, and family to work conflict and negatively related to task interdependence and work to family conflict. Those high on neuroticism, the unconscientious, the job-insecure, those who viewed absence as more legitimate, and those experiencing work-family conflict reported more productivity loss. Overall, the results reveal the value of a behavioral approach to presenteeism over and above a strict medical model.
Uncertainty Visualization Using Copula-Based Analysis in Mixed Distribution Models

Distributions are often used to model uncertainty in many scientific datasets. To preserve the correlation among the spatially sampled grid locations in the dataset, various standard multivariate distribution models have been proposed in visualization literature. These models treat each grid location as a univariate random variable which models the uncertainty at that location. Standard multivariate distributions (both parametric and nonparametric) assume that all the univariate marginals are of the same type/family of distribution. But in reality, different grid locations show different statistical behavior which may not be modeled best by the same type of distribution. In this paper, we propose a new multivariate uncertainty modeling strategy to address the needs of uncertainty modeling in scientific datasets. Our proposed method is based on a statistically sound multivariate technique called Copula, which makes it possible to separate the process of estimating the univariate marginals and the process of modeling dependency, unlike the standard multivariate distributions. The modeling flexibility offered by our proposed method makes it possible to design distribution fields which can have different types of distribution (Gaussian, Histogram, KDE etc.) at the grid locations, while maintaining the correlation structure at the same time. Depending on the results of various standard statistical tests, we can choose an optimal distribution representation at each location, resulting in a more cost efficient modeling without significantly sacrificing on the analysis quality. To demonstrate the efficacy of our proposed modeling strategy, we extract and visualize uncertain features like isocontours and vortices in various real world datasets. We also study various modeling criterion to help users in the task of univariate model selection.
Max-margin Class Imbalanced Learning with Gaussian Affinity

Real-world object classes appear in imbalanced ratios. This poses a significant challenge for classifiers which get biased towards frequent classes. We hypothesize that improving the generalization capability of a classifier should improve learning on imbalanced datasets. Here, we introduce the first hybrid loss function that jointly performs classification and clustering in a single formulation. Our approach is based on an ‘affinity measure’ in Euclidean space that leads to the following benefits: (1) direct enforcement of maximum margin constraints on classification boundaries, (2) a tractable way to ensure uniformly spaced and equidistant cluster centers, (3) flexibility to learn multiple class prototypes to support diversity and discriminability in feature space. Our extensive experiments demonstrate the significant performance improvements on visual classification and verification tasks on multiple imbalanced datasets. The proposed loss can easily be plugged in any deep architecture as a differentiable block and demonstrates robustness against different levels of data imbalance and corrupted labels.
Capless LDO Regulator Achieving −76 dB PSR and 96.3 fs FOM

The performance of switching devices such as display driver ICs is degraded by large power supply noise at switching frequencies from a few hundreds of kilohertz to a few megahertz. In order to minimize the power supply noise, a low-dropout (LDO) regulator with higher power supply rejection (PSR) is essential. In this brief, a capless LDO regulator with a negative capacitance circuit and voltage damper is proposed for enhancing PSR and figure of merit (FOM), respectively, in switching devices. The proposed LDO regulator is fabricated in a  $0.18~\boldsymbol {\mu }\text{m}$  CMOS. Measurement results show that the proposed LDO regulator achieves −76 dB PSR at 1 MHz and 96.3 fs FOM with a total on-chip capacitance of as small as 12.7 pF.
Sentence Compression by Deletion with LSTMs

We present an LSTM approach to deletion-based sentence compression where the task is to translate a sentence into a sequence of zeros and ones, corresponding to token deletion decisions. We demonstrate that even the most basic version of the system, which is given no syntactic information (no PoS or NE tags, or dependencies) or desired compression length, performs surprisingly well: around 30% of the compressions from a large test set could be regenerated. We compare the LSTM system with a competitive baseline which is trained on the same amount of data but is additionally provided with all kinds of linguistic features. In an experiment with human raters the LSTMbased model outperforms the baseline achieving 4.5 in readability and 3.8 in informativeness.
Sine cosine optimization algorithm for feature selection

Nowadays, a dataset includes a huge number of features with irrelevant and redundant ones. Feature selection is required for a better machine-learning algorithms' performance. A system for feature selection is proposed in this work using a sine cosine algorithm (SCA). SCA is a new stochastic search algorithm for optimization problems. SCA optimization adaptively balances the exploration and exploitation to find the optimal solution quickly. The SCA can quickly explore the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The proposed fitness function used incorporates both classification accuracy and feature size reduction. The proposed system was tested on 18 datasets and shows an advance over other search methods as particle swarm optimization (PSO) and genetic algorithm (GA) optimizers commonly used in this context using different evaluation indicators.
Design of Ice-Storage Air Conditioning Control System Based on PLC and Touching Screen

Based on analysis of the structure and control requirements of ice-storage air conditioning system, a distributed control system design was introduced. The hardware environment was mainly based on Programmable Logic Controller ¿PLC¿, and a touching screen was also applied as the local platforms of SCADA ( Supervisory Control and Data Acquisition) ; The software were CX-Programmer 7.1 and EV5000 configuration soft ware respectively. Tests results show that the PLC based control system is not only capable of running stably and reliably, but also has higher control accuracy. The touching screen can communicate precisely with PLC, and monitor and control the statuses of ice-storage air conditioning system promptly via MPI(Multi-Point Interface) protocol.
Centile charts of cervical length between 18 and 32 weeks of gestation.

To establish a centile chart of cervical length between 18 and 32 weeks of gestation in a low-risk population of women.



A prospective longitudinal cohort study of women with a low risk, singleton pregnancy using public healthcare facilities in Cape Town, South Africa. Transvaginal measurement of cervical length was performed between 16 and 32 weeks of gestation and used to construct centile charts. The distribution of cervical length was determined for gestational ages and was used to establish estimates of longitudinal percentiles. Centile charts were constructed for nulliparous and multiparous women together and separately.



Centile estimation was based on data from 344 women. Percentiles showed progressive cervical shortening with increasing gestational age. Averaged over the entire follow-up period, mean cervical length was 1.5 mm shorter in nulliparous women compared with multiparous women (95% CI, 0.4-2.6).



Establishment of longitudinal reference values of cervical length in a low-risk population will contribute toward a better understanding of cervical length in women at risk for preterm labor.
Triangulating a cognitive control network using diffusion-weighted magnetic resonance imaging (MRI) and functional MRI.

The ability to stop motor responses depends critically on the right inferior frontal cortex (IFC) and also engages a midbrain region consistent with the subthalamic nucleus (STN). Here we used diffusion-weighted imaging (DWI) tractography to show that the IFC and the STN region are connected via a white matter tract, which could underlie a "hyperdirect" pathway for basal ganglia control. Using a novel method of "triangulation" analysis of tractography data, we also found that both the IFC and the STN region are connected with the presupplementary motor area (preSMA). We hypothesized that the preSMA could play a conflict detection/resolution role within a network between the preSMA, the IFC, and the STN region. A second experiment tested this idea with functional magnetic resonance imaging (fMRI) using a conditional stop-signal paradigm, enabling examination of behavioral and neural signatures of conflict-induced slowing. The preSMA, IFC, and STN region were significantly activated the greater the conflict-induced slowing. Activation corresponded strongly with spatial foci predicted by the DWI tract analysis, as well as with foci activated by complete response inhibition. The results illustrate how tractography can reveal connections that are verifiable with fMRI. The results also demonstrate a three-way functional-anatomical network in the right hemisphere that could either brake or completely stop responses.
A working spatio-temporal model of the human visual system for image restoration and quality assessment applications

This paper describes a spatio-temporal model of the human visual system (HVS) for video imaging applications, predicting the response of the neurons of the primary visual cortex. The model simulates the behavior of the HVS with a three-dimensional lter bank which decomposes the data into perceptual channels, each one being tuned to a speciic spatial frequency, orientation and temporal frequency. It further accounts for contrast sensitivity, inter-stimuli masking and spatio-temporal interaction. The free parameters of the model have been estimated by psychophysics. The model can then be used as the basis for many applications. As an example, a quality metric for coded video sequences is presented.
What are dynamic optimization problems?

Dynamic Optimization Problems (DOPs) have been widely studied using Evolutionary Algorithms (EAs). Yet, a clear and rigorous definition of DOPs is lacking in the Evolutionary Dynamic Optimization (EDO) community. In this paper, we propose a unified definition of DOPs based on the idea of multiple-decision-making discussed in the Reinforcement Learning (RL) community. We draw a connection between EDO and RL by arguing that both of them are studying DOPs according to our definition of DOPs. We point out that existing EDO or RL research has been mainly focused on some types of DOPs. A conceptualized benchmark problem, which is aimed at the systematic study of various DOPs, is then developed. Some interesting experimental studies on the benchmark reveal that EDO and RL methods are specialized in certain types of DOPs and more importantly new algorithms for DOPs can be developed by combining the strength of both EDO and RL methods.
Cannabinoid receptors and the regulation of bone mass.

A functional endocannabinoid system is present in several mammalian organs and tissues. Recently, endocannabinoids and their receptors have been reported in the skeleton. Osteoblasts, the bone forming cells, and osteoclasts, the bone resorbing cells, produce the endocannabinoids anandamide and 2-arachidonoylglycerol and express CB2 cannabinoid receptors. Although CB2 has been implicated in pathological processes in the central nervous system and peripheral tissues, the skeleton appears as the main system physiologically regulated by CB2. CB2-deficient mice show a markedly accelerated age-related bone loss and the CNR2 gene (encoding CB2) in women is associated with low bone mineral density. The activation of CB2 attenuates ovariectomy-induced bone loss in mice by restraining bone resorption and enhancing bone formation. Hence synthetic CB2 ligands, which are stable and orally available, provide a basis for developing novel anti-osteoporotic therapies. Activation of CB1 in sympathetic nerve terminals in bone inhibits norepinephrine release, thus balancing the tonic sympathetic restrain of bone formation. Low levels of CB1 were also reported in osteoclasts. CB1-null mice display a skeletal phenotype that is dependent on the mouse strain, gender and specific mutation of the CB1 encoding gene, CNR1.
Visualizing the Simple Bayesian Classi er

The simple Bayesian classi er (SBC), sometimes called Naive-Bayes, is built based on a conditional independence model of each attribute given the class. The model was previously shown to be surprisingly robust to obvious violations of this independence assumption, yielding accurate classi cation models even when there are clear conditional dependencies. The SBC can serve as an excellent tool for initial exploratory data analysis when coupled with a visualizer that makes its structure comprehensible. We describe such a visual representation of the SBC model that has been successfully implemented. We describe the requirements we had for such a visualization and the design decisions we made to satisfy them.
The role of sensorimotor impairments in dyslexia: a multiple case study of dyslexic children.

This study attempts to investigate the role of sensorimotor impairments in the reading disability that characterizes dyslexia. Twenty-three children with dyslexia were compared to 22 control children, matched for age and non-verbal intelligence, on tasks assessing literacy as well as phonological, visual, auditory and motor abilities. The dyslexic group as a whole were significantly impaired on phonological, but not sensorimotor, tasks. Analysis of individual data suggests that the most common impairments were on phonological and visual stress tasks and the vast majority of dyslexics had one of these two impairments. Furthermore, phonological skill was able to account for variation in literacy skill, to the exclusion of all sensorimotor factors, while neither auditory nor motor skill predicted any variance in phonological skill. Visual stress seems to account for a small proportion of dyslexics, independently of the commonly reported phonological deficit. However, there is little evidence for a causal role of auditory, motor or other visual impairments.