nils-herrmann/clean_scirepeval huggingface dataset:
A novel corner-fed patch to reduce cross-polarization for a microstrip antenna array

This paper presents a novel corner-fed microstrip square patch. On the patch there is a slot along the diagonal. With this slot, the current distribution on the patch is changed. The currents related to co-polarization become more dominant and those related to cross-polarization become more symmetrical. These two factors result in a reduction in the cross-polarization level. An eight element shunt connected series-fed linear array has been designed, manufactured and tested. The measured results show that the cross-polarization level is reduced by 10.5 dB, while the other performances of the array are nearly the same as those of a standard one.
Process Mining in IT Service Management: A Case Study

The explosion of process-related data in nowadays organizations has raised the interest to exploit the data in order to know in deep how the business processes are being carried out. To face this need, process mining has emerged as a way to analyze the behavior of an organization by extracting knowledge from process related data. In this paper, we present a case study of process mining in a real IT service management scenario. We describe an exploratory analysis of real life event logs generated between 2012 and 2015, in four different processes designed within an IT platform. More specifically, we analyze the way of handling the different requests and incidents registered in an organization.
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting

Stock market prediction is regarded as a challenging task in financial time-series forecasting. The central idea to successful stock market prediction is achieving best results using minimum required input data and the least complex stock market model. To achieve these purposes this article presents an integrated approach based on genetic fuzzy systems (GFS) and artificial neural networks (ANN) for constructing a stock price forecasting expert system. At first, we use stepwise regression analysis (SRA) to determine factors which have most influence on stock prices. At the next stage we divide our raw data into k clusters by means of self-organizing map (SOM) neural networks. Finally, all clusters will be fed into independent GFS models with the ability of rule base extraction and data base tuning. We evaluate capability of the proposed approach by applying it on stock price data gathered from IT and Airlines sectors, and compare the outcomes with previous stock price forecasting methods using mean absolute percentage error (MAPE). Results show that the proposed approach outperforms all previous methods, so it can be considered as a suitable tool for stock price forecasting problems.
Benefits of visualization in the Mammography Problem

Trying to make a decision between two outcomes, when there is some level of uncertainty, is inherently difficult because it involves probabilistic reasoning. Previous studies have shown that most people do not correctly apply Bayesian inference to solve probabilistic problems for decision making under uncertainty. In an effort to improve decision making with Bayesian problems, previous work has studied supplementing the textual description of problems with visualizations, such as graphs and charts. However, results have been varied and generally indicate that visualization is not an effective technique. As these studies were performed over many years with a variety of goals and experimental conditions, we sought to re-evaluate the use of visualization as an aid in solving Bayesian problems. Many of these studies used the classic Mammography Problem with visualizations portraying the problem structure, the quantities involved, or the nested-set relations of the populations involved. We selected three representative visualizations from this work and developed two hybrid visualizations, combining structure types and frequency with structure. We also included a text-only baseline condition and a text-legend condition where all nested-set problem values were given to eliminate the need for participants to estimate or calculate values. Seven hundred participants evaluated these seven conditions on the classic Mammography Problem in a crowdsourcing system, where micro-interaction data was collected from the participants. Our analysis of the user input and of the results indicates that participants made use of the visualizations but that the visualizations did not help participants to perform more accurately. Overall, static visualizations do not seem to aid a majority of people in solving the Mammography Problem. & 2015 Elsevier Ltd. All rights reserved.
Generalized Attacker and Attack Models for Cyber Physical Systems

An attacker model is proposed for Cyber Physical Systems (CPS). The attack models derived from the attacker model are used to generate parameterized attack procedures and functions that target a specific CPS. The proposed models capture both physical and cyber attacks and unify a number of existing attack models into a common framework useful for researchers in the experimental assessment of attack detection techniques. The generality of the models is shown by mapping a broad variety of existing attack models to the models proposed here, as well as generating attacks that are not found in the CPS design literature. The models have been used extensively in understanding the impact of cyber attacks on a water treatment system and in the design and assessment of detection mechanisms.
Transformerless micro-inverter for grid-connected photovoltaic systems

The leakage currents caused by high-frequency common-mode (CM) voltage have become a major concern in transformerless photovoltaic (PV) inverters. This paper addresses to a review on dc-ac converters applied to PV systems that can avoid the circulation of leakage currents. Looking for a lower cost and higher reliability solution, a 250 W PV transformerless micro-inverter prototype based on the bipolar full-bridge topology was built and tested. As it is confirmed by experimental results, this topology is able to maintain the CM voltage constant and prevent the circulation of CM currents through the circuit.
A PRACTICAL MINIATURIZED U-SLOT PATCH ANTENNA WITH ENHANCED BANDWIDTH

In this paper, an asymmetric U-slot patch antenna with low probe diameter is presented. It will be shown that reduction in probe diameter causes in reduction in bandwidth. One of the characteristics of this antenna is keeping the bandwidth in 30% in spite of reduction in antenna size and use of low probe diameter compared to antenna presented in . The presented antenna in this paper has been fabricated by pcb technique and tested. The far-field results have also been presented based on simulation and measurement. Although the antenna has high cross polarisation level, in the case of using circular polarisation, the use of this antenna can be recommended because of its reduced size, high impedance bandwidth, high total gain in spite of having low size, and ease of fabrication.
A 5.8-GHz DSRC Transceiver With a 10-$\mu{\hbox {A}}$  Interference-Aware Wake-Up Receiver for the Chinese ETCS

This paper presents a fully integrated 5.8-GHz dedicated short-range communication transceiver with a 10- μA interference-aware wake-up receiver (WuRx) for Chinese electronic toll collection system terminals that can operate with a low standby and operating current consumption. To reduce the current consumption, a high-gain RF envelope detector using a voltage-boosting method is proposed for both the WuRx and receiver (Rx) while the proposed high-power ASK modulator extends output dynamic range in low power consumption. Additionally, a delay-based bandpass filter is adopted in the WuRx to filter out interference from automotive applications, thus increasing the battery lifetime by reducing the probability of a false wake-up. The proposed transceiver is fabricated using 0.13- μm CMOS technology with a chip size of 2.8 mm2 for the target frequency range of 5.8 GHz. The measured results demonstrate sensitivities of -44 and -61 dBm for the WuRx and Rx, dissipating currents of 10 μA and 19 mA from 3.3-V supply voltage, respectively. The transmitter exhibits a normal output power of +5 dBm at an operating current of 46 mA.
Compliance with a structured bedside handover protocol : An observational , multicentred study ☆

Bedside handover is the delivery of the nurse-to-nurse shift handover at the patient’s bedside. The method is increasingly used in nursing, but the evidence concerning the implementation process and compliance to the method is limited.  To determine the compliance with a structured bedside handover protocol following ISBARR and if there were differences in compliance between wards.  A multicentred observational study with unannounced and non-participatory observations (n=638) one month after the implementation of a structured bedside handover protocol.  Observations of individual patient handovers between nurses from the morning shift and the afternoon shift in 12 nursing wards in seven hospitals in Flanders, Belgium.  A tailored and structured bedside handover protocol following ISBARR was developed, and nurses were trained accordingly. One month after implementation, a minimum of 50 observations were performed with a checklist, in each participating ward. To enhance reliability, 20% of the observations were conducted by two researchers, and inter-rater agreement was calculated. Data were analysed using descriptive statistics, one-way ANOVAs and multilevel analysis.  Average compliance rates to the structured content protocol during bedside handovers were high (83.63%; SD 11.44%), and length of stay, the type of ward and the nursing care model were influencing contextual factors. Items that were most often omitted included identification of the patient (46.27%), the introduction of nurses (36.51%), hand hygiene (35.89%), actively involving the patient (34.44%), and using the call light (21.37%). Items concerning the exchange of clinical information (e.g., test results, reason for admittance, diagnoses) were omitted less (8.09%–1.45%). Absence of the patients (27.29%) and staffing issues (26.70%) accounted for more than half of the non-executed bedside handovers. On average, a bedside handover took 146 s per patient.  When the bedside handover was delivered, compliance to the structured content was high, indicating that the execution of a bedside handover is a feasible step for nurses. The compliance rate was influenced by the patient’s length of stay, the nursing care model and the type of ward, but their influence was limited. Future implementation projects on bedside handover should focus sufficiently on standard hospital procedures and patient involvement. According to the nurses, there was however a high number of situations where bedside handovers could not be delivered, perhaps indicating a reluctance in practice to use bedside
Semantic Labeling: A Domain-Independent Approach

Semantic labeling is the process of mapping attributes in data sources to classes in an ontology and is a necessary step in heterogeneous data integration. Variations in data formats, attribute names and even ranges of values of data make this a very challenging task. In this paper, we present a novel domain-independent approach to automatic semantic labeling that uses machine learning techniques. Previous approaches use machine learning to learn a model that extracts features related to the data of a domain, which requires the model to be re-trained for every new domain. Our solution uses similarity metrics as features to compare against labeled domain data and learns a matching function to infer the correct semantic labels for data. Since our approach depends on the learned similarity metrics but not the data itself, it is domainindependent and only needs to be trained once to work effectively across multiple domains. In our evaluation, our approach achieves higher accuracy than other approaches, even when the learned models are trained on domains other than the test domain.
Signal quality measures for pulse oximetry through waveform morphology analysis.

Pulse oximetry has been extensively used to estimate oxygen saturation in blood, a vital physiological parameter commonly used when monitoring a subject's health status. However, accurate estimation of this parameter is difficult to achieve when the fundamental signal from which it is derived, the photoplethysmograph (PPG), is contaminated with noise artifact induced by movement of the subject or the measurement apparatus. This study presents a novel method for automatic rejection of artifact contaminated pulse oximetry waveforms, based on waveform morphology analysis. The performance of the proposed algorithm is compared to a manually annotated gold standard. The creation of the gold standard involved two experts identifying sections of the PPG signal containing good quality PPG pulses and/or noise, in 104 fingertip PPG signals, using a simultaneous electrocardiograph (ECG) signal as a reference signal. The fingertip PPG signals were each 1 min in duration and were acquired from 13 healthy subjects (10 males and 3 females). Each signal contained approximately 20 s of purposely induced artifact noise from a variety of activities involving subject movement. Some unique waveform morphology features were extracted from the PPG signals, which were believed to be correlated with signal quality. A simple decision-tree classifier was employed to arrive at a classification decision, at a pulse-by-pulse resolution, of whether a pulse was of acceptable quality for use or not. The performance of the algorithm was assessed using Cohen's kappa coefficient (κ), sensitivity, specificity and accuracy measures. A mean κ of 0.64 ± 0.22 was obtained, while the mean sensitivity, specificity and accuracy were 89 ± 10%, 77 ± 19% and 83 ± 11%, respectively. Furthermore, a heart rate estimate, extracted from uncontaminated sections of PPG, as identified by the algorithm, was compared with the heart rate derived from an uncontaminated simultaneous ECG signal. The mean error between both heart rate readings was 0.49 ± 0.66 beats per minute (BPM), in comparison to an error value observed without using the artifact detection algorithm of 7.23 ± 5.78 BPM. These results demonstrate that automated identification of signal artifact in the PPG signal through waveform morphology analysis is achievable. In addition, a clear improvement in the accuracy of the derived heart rate is also evident when such methods are employed.
A Web-based Tool for the Integrated Annotation of Semantic and Syntactic Structures

We introduce the third major release of WebAnno, a generic web-based annotation tool for distributed teams. New features in this release focus on semantic annotation tasks (e.g. semantic role labelling or event annotation) and allow the tight integration of semantic annotations with syntactic annotations. In particular, we introduce the concept of slot features, a novel constraint mechanism that allows modelling the interaction between semantic and syntactic annotations, as well as a new annotation user interface. The new features were developed and used in an annotation project for semantic roles on German texts. The paper briefly introduces this project and reports on experiences performing annotations with the new tool. On a comparative evaluation, our tool reaches significant speedups over WebAnno 2 for a semantic annotation task.
Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review

In recent years, deep convolutional neural networks (CNNs) have shown record-shattering performance in a variety of computer vision problems, such as visual object recognition, detection and segmentation. These methods have also been utilised in medical image analysis domain for lesion segmentation, anatomical segmentation and classification. We present an extensive literature review of CNN techniques applied in brain magnetic resonance imaging (MRI) analysis, focusing on the architectures, pre-processing, data-preparation and post-processing strategies available in these works. The aim of this study is three-fold. Our primary goal is to report how different CNN architectures have evolved, discuss state-of-the-art strategies, condense their results obtained using public datasets and examine their pros and cons. Second, this paper is intended to be a detailed reference of the research activity in deep CNN for brain MRI analysis. Finally, we present a perspective on the future of CNNs in which we hint some of the research directions in subsequent years.
BeatCoin: Leaking Private Keys from Air-Gapped Cryptocurrency Wallets

Cryptocurrency wallets store the wallets private key(s), and hence, are a lucrative target for attackers. With possession of the private key, an attacker virtually owns all of the currency in the compromised wallet. Managing cryptocurrency wallets offline, in isolated (’air-gapped’) computers, has been suggested in order to secure the private keys from theft. Such air-gapped wallets are often referred to as ’cold wallets.’ In this paper we show how private keys can be exfiltrated from air-gapped wallets. In the adversarial attack model, the attacker infiltrates the offline wallet, infecting it with malicious code. The malware can be preinstalled or pushed in during the initial installation of the wallet, or it can infect the system when removable media (e.g., USB flash drive) is inserted into the wallet’s computer in order to sign a transaction. These attack vectors have repeatedly been proven feasible in the last decade (e.g., [1],[2],[3],[4],[5],[6],[7],[8],[9],[10]). Having obtained a foothold in the wallet, an attacker can utilize various air-gap covert channel techniques (bridgeware [11]) to jump the airgap and exfiltrate the wallets private keys. We evaluate various exfiltration techniques, including physical, electromagnetic, electric, magnetic, acoustic, optical, and thermal techniques. This research shows that although cold wallets provide a high degree of isolation, its not beyond the capability of motivated attackers to compromise such wallets and steal private keys from them. We demonstrate how a 256-bit private key (e.g., bitcoin’s private keys) can be exfiltrated from an offline, air-gapped wallet of a fictional character named Satoshi within a matter of seconds.
Fast approximation of matrix coherence and statistical leverage

The statistical leverage scores of a matrix A are the squared row-norms of the matrix containing its (top) left singular vectors and the coherence is the largest leverage score. These quantities are of interest in recently-popular problems such as matrix completion and Nyström-based low-rank matrix approximation as well as in large-scale statistical data analysis applications more generally; moreover, they are of interest since they define the key structural nonuniformity that must be dealt with in developing fast randomized matrix algorithms. Our main result is a randomized algorithm that takes as input an arbitrary n× d matrix A, with n≫ d, and that returns as output relative-error approximations to all n of the statistical leverage scores. The proposed algorithm runs (under assumptions on the precise values of n and d) in O(nd logn) time, as opposed to the O(nd) time required by the naı̈ve algorithm that involves computing an orthogonal basis for the range of A. Our analysis may be viewed in terms of computing a relative-error approximation to an underconstrained least-squares approximation problem, or, relatedly, it may be viewed as an application of Johnson-Lindenstrauss type ideas. Several practically-important extensions of our basic result are also described, including the approximation of so-called cross-leverage scores, the extension of these ideas to matrices with n≈ d, and the extension to streaming environments.
Knowledge Engineering and Knowledge Management

Recently, experts and practitioners in language resources have started recognizing the benefits of the linked data (LD) paradigm for the representation and exploitation of linguistic data on the Web. The adoption of the LD principles is leading to an emerging ecosystem of multilingual open resources that conform to the Linguistic Linked Open Data Cloud, in which datasets of linguistic data are interconnected and represented following common vocabularies, which facilitates linguistic information discovery, integration and access. In order to contribute to this initiative, this paper summarizes several key aspects of the representation of linguistic information as linked data from a practical perspective. The main goal of this document is to provide the basic ideas and tools for migrating language resources (lexicons, corpora, etc.) as LD on the Web and to develop some useful NLP tasks with them (e.g., word sense disambiguation). Such material was the basis of a tutorial imparted at the EKAW’14 conference, which is also reported in the paper.
Analysis of skin color models for face detection

This paper describes face detection method using skin color information. Processing color information is faster than processing other facial features. There are different color models present for skin color segmentation like RGB, CMY, YUV, YIQ, YCbCr, YCgCr, HSI, etc. But to choose best option among these models for efficient skin color segmentation is difficult task. In this paper we have explored and analyse three most popular color models-YCbCr, YCgCr and HSI for face detection.
Using prosody to avoid ambiguity : Effects of speaker awareness and referential context

In three experiments, a referential communication task was used to determine the conditions under which speakers produce and listeners use prosodic cues to distinguish alternative meanings of a syntactically ambiguous phrase. Analyses of the actions and utterances from Experiments 1 and 2 indicated that Speakers chose to produce effective prosodic cues to disambiguation only when the referential scene provided support for both interpretations of the phrase. In Experiment 3, on-line measures of parsing commitments were obtained by recording the Listener!s eye movements to objects as the Speaker gave the instructions. Results supported the previous experiments but also showed that the Speaker!s prosody affected the Listener!s interpretation prior to the onset of the ambiguous phrase, thus demonstrating that prosodic cues not only influence initial parsing but can also be used to predict material which has yet to be spoken. The findings suggest that informative prosodic cues depend upon speakers! knowledge of the situation: speakers provide prosodic cues when needed; listeners use these prosodic cues when present. ! 2002 Elsevier Science (USA). All rights reserved.
High Beam Steering in Fabry–Pérot Leaky-Wave Antennas

A high-gain low-profile Fabry-Perot (FP) leaky-wave antenna (LWA) presenting one-dimensional high beam steering properties is proposed in this letter. The structure consists of a ground plane and a varying inductive partially reflective surface (PRS). A microstrip patch antenna is embedded into the cavity to act as the primary feed. As design examples, antennas are designed to operate at 9.5 GHz. Subwavelength FP cavities with fixed overall thickness of λ0 /6 (where λ0 is the free-space operating wavelength) are fabricated and measured. The impact of varying the PRS inductance is analyzed. It is shown that a high beam steering angle from broadside toward endfire direction close to 60 can be obtained when judiciously designing the inductive grid of the PRS.