nils-herrmann/clean_scirepeval huggingface dataset:
The dark side of open access in Google and Google Scholar: the case of Latin-American repositories

Since repositories are a key tool in making scholarly knowledge open access (OA), determining their web presence and visibility on the Web (both are proxies of web impact) is essential, particularly in Google (search engine par excellence) and Google Scholar (a tool increasingly used by researchers to search for academic information). The few studies conducted so far have been limited to very specific geographic areas (USA), which makes it necessary to find out what is happening in other regions that are not part of mainstream academia, and where repositories play a decisive role in the visibility of scholarly production. The main objective of this study is to ascertain the web presence and visibility of Latin American repositories in Google and Google Scholar through the application of page count and web mention indicators respectively. For a sample of 137 repositories, the results indicate that the indexing ratio is low in Google, and virtually nonexistent in Google Scholar; they also indicate a complete lack of correspondence between the repository records and the data produced by these two search tools. These results are mainly attributable to limitations arising from the use of description schemas that are incompatible with Google Scholar (repository design) and the reliability of web mention indicators (search engines). We conclude that neither Google nor Google Scholar accurately represent the actual size of OA content published by Latin American repositories; this may indicate a non-indexed, hidden side to OA, which could be limiting the dissemination and consumption of OA scholarly literature.
BlindBox: Deep Packet Inspection over Encrypted Traffic

Many network middleboxes perform deep packet inspection (DPI), a set of useful tasks which examine packet payloads. These tasks include intrusion detection (IDS), exfiltration detection, and parental filtering. However, a long-standing issue is that once packets are sent over HTTPS, middleboxes can no longer accomplish their tasks because the payloads are encrypted. Hence, one is faced with the choice of only one of two desirable properties: the functionality of middleboxes and the privacy of encryption. We propose BlindBox, the first system that simultaneously provides {\em both} of these properties. The approach of BlindBox is to perform the deep-packet inspection {\em directly on the encrypted traffic. BlindBox realizes this approach through a new protocol and new encryption schemes.
 We demonstrate that BlindBox enables applications such as IDS, exfiltration detection and parental filtering, and supports real rulesets from both open-source and industrial DPI systems. We implemented BlindBox and showed that it is practical for settings with long-lived HTTPS connections. Moreover, its core encryption scheme is 3-6 orders of magnitude faster than existing relevant cryptographic schemes.
Algorithmic approaches to protein-protein interaction site prediction

Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented.
Tracing Research Paradigm Change Using Terminological Methods. A Case Study on "Machine Translation" in the ACL Anthology Reference Corpus

This paper explores the use of terminology extraction methods for detecting paradigmatic changes in scientific articles. We use a statistical method for identifying salient nouns and adjectives that signal these paradigmatic changes. We then employ the extracted lexical units for discovering terms that are assumed to be central in characterising paradigm shifts. To assess the method’s performance, in this pilot study, we work on “machine translation” (MT) research articles sampled from the ACL anthology reference corpus. We analyse this corpus to check whether the proposed approach can trace the dramatic changes that machine translation research has experienced in the last decades: from transformational rule-based methods to statistical machine learning-based techniques.
Comparing Fusion Models for DNN-Based Audiovisual Continuous Speech Recognition

Audiovisual fusion is one of the most challenging tasks that continues to attract substantial research interest in the field of audiovisual automatic speech recognition AV-ASR. In the last few decades, many approaches for integrating the audio and video modalities were proposed to enhance the performance of automatic speech recognition in both clean and noisy conditions. However, very few studies can be found in the literature that compare different fusion models for AV-ASR. Even less research work compares audiovisual fusion models for large vocabulary continuous speech recognition LVCSR models using deep neural networks DNNs. This paper reviews and compares the performance of five audiovisual fusion models: the feature fusion model, the decision fusion model, the multistream hidden Markov model HMM, the coupled HMM, and the turbo decoders. A complete evaluation of these fusion models is conducted using a standard speaker-independent DNN-based LVCSR Kaldi recipe in three experimental setups: a clean-train-clean-test, a clean-train-noisy-test, and a matched-training setup. All experiments have been applied to the recently released NTCD-TIMIT audiovisual corpus. The task of NTCD-TIMIT is phone recognition in continuous speech. Using NTCD-TIMIT with its freely available visual features and 37 clean and noisy acoustic signals allows for this study to be a common benchmark, to which novel LVCSR AV-ASR models and approaches can be compared.
Human Activity Recognition Using Temporal Convolutional Network

Human activity recognition using wearable sensors is an area of interest for various domains like healthcare, surveillance etc. Various approaches have been used to solve the problem of activity recognition. Recently deep learning methods like RNNs and LSTMs have been used for this task. But these architectures are unable to capture long term dependencies in time series data. In this work, we propose to use the Temporal Convolutional Network architecture for recognizing the activities from the sensor data obtained from a smartphone. Due to the potential of the architecture to take variable length input sequences along with significantly better ability to capture long term dependencies, it performs better than other deep learning methods. The results of the proposed methods shows an improved performance over the existing methods.
PRF-ambiguity resolving by wavelength diversity

For high precision Synthetic Aperture Radar (SAR) processing, the determination of the Doppler centroid is indispensable. The Doppler frequency estimated from azimuth spectra, however, suffers from the fact that the data are sampled with the pulse repetition frequency (PRF) and an ambiguity about the correct PRF band remains. A new algorithm to resolve this ambiguity is proposed. It uses the fact that the Doppler centroid depends linearly on the transmitted radar frequency for a given antenna squint angle. This dependence is not subject to PRF ambiguities. It can be measured by Fourier transforming the SAR data in the range direction and estimating the Doppler centroid at each range frequency. The achievable accuracy is derived theoretically and verified with Seasat data of different scene content. The algorithm works best with low contrast scenes, where the conventional look correlation technique fails. It needs no iterative processing of the SAR data and causes only low computational load.
Predicting evolutionary patterns of mammalian teeth from development

One motivation in the study of development is the discovery of mechanisms that may guide evolutionary change. Here we report how development governs relative size and number of cheek teeth, or molars, in the mouse. We constructed an inhibitory cascade model by experimentally uncovering the activator–inhibitor logic of sequential tooth development. The inhibitory cascade acts as a ratchet that determines molar size differences along the jaw, one effect being that the second molar always makes up one-third of total molar area. By using a macroevolutionary test, we demonstrate the success of the model in predicting dentition patterns found among murine rodent species with various diets, thereby providing an example of ecologically driven evolution along a developmentally favoured trajectory. In general, our work demonstrates how to construct and test developmental rules with evolutionary predictability in natural systems.
Secure storage system and key technologies

With the rapid development of cloud storage, data security in storage receives great attention and becomes the top concern to block the spread development of cloud service. In this paper, we systematically study the security researches in the storage systems. We first present the design criteria that are used to evaluate a secure storage system and summarize the widely adopted key technologies. Then, we further investigate the security research in cloud storage and conclude the new challenges in the cloud environment. Finally, we give a detailed comparison among the selected secure storage systems and draw the relationship between the key technologies and the design criteria.
Effect of Yoga on the Autonomic Nervous System : Clinical Implications in the Management of Atrial Fibrillation

Atrial fibrillation (AF), the most common sustained cardiac arrhythmia, is seen in approximately 1.5% of the U.S. population  and results in substantial morbidity and mortality . One of the largest U.S. epidemiological studies, the Framingham Heart Study, predicted that AF prevalence doubles with each advancing decade of age, from 0.5% at age 50-59 years to almost 9% at age 80-89 years, independent of the increasing prevalence of known predisposing conditions . Although medical treatment involving radiofrequency catheter ablation has become the well-accepted management strategy for AF  failure of this therapy is common, with only two-thirds or less of the patients treated remaining free of AF on long-term followup . Early recurrence of atrial tachyarrhythmia, usually defined as arrhythmia recurrence within the first 3 months following ablation, is frequently associated with late recurrence of atrial tachyarrhythmia . Acute myocardial injury and the subsequent inflammatory response, as well as modifications of the cardiac autonomic nervous system, provide an early and potentially reversible pro-arrhythmic substrate because of altered atrial myocardial conduction and refractoriness . Research has shown that psychological stressors and imbalance in the autonomic nervous system (ANS) are the most common triggers for paroxysmal AF [6,7]. The mind-body therapy yoga has been shown to reduce stress and maintain autonomic nervous system balance : hence, use of complementary health
Cone-constrained eigenvalue problems: theory and algorithms

Equilibria in mechanics or in transportation models are not always expressed through a system of equations, but sometimes they are characterized by means of complementarity conditions involving a convex cone. This work deals with the analysis of cone-constrained eigenvalue problems. We discuss some theoretical issues like, for instance, the estimation of the maximal number of eigenvalues in a cone-constrained problem. Special attention is paid to the Paretian case. As a short addition to the theoretical part, we introduce and study two algorithms for solving numerically such type of eigenvalue problems.
Index Modulation with PAPR and Beamforming for 5G MIMO-OFDM

Although key techniques for next-generation wireless communication have been explored separately, relatively little work has been done to investigate their potential cooperation for performance optimization. To address this problem, we propose a holistic framework for robust 5G communication based on multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM). More specifically, we design a new framework that supports: 1) index modulation based on OFDM (OFDM–M) ; 2) sub-band beamforming and channel estimation to achieve massive path gains by exploiting multiple antenna arrays ; and 3) sub-band pre-distortion for peak-to-average-power-ratio (PAPR) reduction  to significantly decrease the PAPR and communication errors in OFDM-IM by supporting a linear behavior of the power amplifier in the modem. The performance of the proposed framework is evaluated against the state-of-the-art QPSK, OFDM-IM  and QPSK-spatiotemporal QPSK-ST [2] schemes. The results show that our framework reduces the bit error rate (BER), mean square error (MSE) and PAPR compared to the baselines by approximately 6–13dB, 8–13dB, and 50%, respectively.
Mining Causality of Network Events in Log Data

Network log messages (e.g., syslog) are expected to be valuable and useful information to detect unexpected or anomalous behavior in large scale networks. However, because of the huge amount of system log data collected in daily operation, it is not easy to extract pinpoint system failures or to identify their causes. In this paper, we propose a method for extracting the pinpoint failures and identifying their causes from network syslog data. The methodology proposed in this paper relies on causal inference that reconstructs causality of network events from a set of time series of events. Causal inference can filter out accidentally correlated events, thus it outputs more plausible causal events than traditional cross-correlation-based approaches can. We apply our method to 15 months’ worth of network syslog data obtained from a nationwide academic network in Japan. The proposed method significantly reduces the number of pseudo correlated events compared with the traditional methods. Also, through three case studies and comparison with trouble ticket data, we demonstrate the effectiveness of the proposed method for practical network operation.
Enterprise information systems project implementation : : A case study of ERP in Rolls-Royce

Economic globalisation and internationalisation of operations are essential factors in integration of suppliers, partners and customers within and across national borders, the objective being to achieve integrated supply chains. In this effort, implementation of information technologies and systems such as enterprise resource planning (ERP) facilitate the desired level of integration. There are cases of successful and unsuccessful implementations. The principal reason for failure is often associated with poor management of the implementation process. This paper examines key dimensions of implementation of ERP system within a large manufacturing organisation and identifies core issues to confront in successful implementation of enterprise information system. A brief overview of the application of ERP system is also presented and in particular, ERP software package known as SAP R/3, which was the ERP software package selected by a c Purchase Export Previous article Next article Check if you have access through your login credentials or your institution.
Auricular reconstruction of congenital microtia:
personal experience in 225 cases

Microtia is a congenital disease with various degrees of severity, ranging from the presence of rudimentary and malformed vestigial structures to the total absence of the ear (anotia). The complex anatomy of the external ear and the necessity to provide good projection and symmetry make this reconstruction particularly difficult. The aim of this work is to report our surgical technique of microtic ear correction and to analyse the short and long term results. From 2000 to 2013, 210 patients affected by microtia were treated at the Maxillo-Facial Surgery Division, Head and Neck Department, University Hospital of Parma. The patient population consisted of 95 women and 115 men, aged from 7 to 49 years. A total of 225 reconstructions have been performed in two surgical stages basing of Firmin's technique with some modifications and refinements. The first stage consists in fabrication and grafting of a three-dimensional costal cartilage framework. The second stage is performed 5-6 months later: the reconstructed ear is raised up and an additional cartilaginous graft is used to increase its projection. A mastoid fascial flap together with a skin graft are then used to protect the cartilage graft. All reconstructions were performed without any major complication. The results have been considered satisfactory by all patients starting from the first surgical step. Low morbidity, the good results obtained and a high rate of patient satisfaction make our protocol an optimal choice for treatment of microtia. The surgeon's experience and postoperative patient care must be considered as essential aspects of treatment.
Pharmacokinetics of Mirabegron, a β3-Adrenoceptor Agonist for Treatment of Overactive Bladder, in Healthy Japanese Male Subjects: Results from Single- and Multiple-Dose Studies

Mirabegron is a human β3-adrenoceptor agonist for the treatment of overactive bladder. The pharmacokinetic profile of mirabegron has been extensively characterized in healthy Caucasian subjects.



The objective of this study was to evaluate the pharmacokinetics, dose-proportionality, and tolerability of mirabegron following single and multiple oral doses in healthy Japanese male subjects. The results were compared with those reported in non-Japanese (primarily Caucasian) subjects.



Two studies were conducted. In a single-blind, randomized, placebo-controlled, parallel-group, single- and multiple-ascending dose study (Study 1), mirabegron oral controlled absorption system (OCAS) tablets were administered at single doses of 50, 100, 200, 300, and 400 mg, with eight subjects (six active, two placebo) per dose group (Part I), and once daily for 7 days at 100 and 200 mg with 12 subjects (eight active, four placebo) per group (Part II). In an open-label, three-period, single-ascending dose study (Study 2), mirabegron OCAS was administered to 12 subjects at 25, 50, and 100 mg in an intra-subject dose-escalation design. Plasma and/or urine samples were collected up to 72 h after the first and last dose and analyzed for mirabegron. Pharmacokinetic parameters were determined using non-compartmental methods. Tolerability assessments included physical examinations, vital signs, 12-lead electrocardiogram, clinical laboratory tests (biochemistry, hematology, and urinalysis), and adverse event (AE) monitoring.



Forty and 24 young male subjects completed Part I and II, respectively, of Study 1. Twelve young males completed Study 2. After single oral doses (25-400 mg), maximum plasma concentrations (C max) were reached at approximately 2.8-4.0 h postdose. Plasma exposure (C max and area under the plasma concentration-time curve) of mirabegron increased more than dose proportionally at single doses of 25-100 mg and approximately dose proportionally at high doses of 300 and 400 mg. A more than dose proportional increase in plasma exposure was noted in the body of the same individual. Mirabegron accumulated twofold upon once-daily dosing relative to single-dose data. Steady state was reached within 7 days. Mirabegron was generally well-tolerated at single doses up to 400 mg and multiple doses up to 200 mg. The AE with the highest incidence was increased pulse rate at 400 mg in Study 1.



Mirabegron OCAS exhibits similar single- and multiple-dose pharmacokinetic characteristics and deviations from dose proportionality in healthy Japanese male subjects compared with those observed in non-Japanese (primarily Caucasian) subjects in previous studies.
VOLCANIC ERUPTIONS AND CLIMATE

Volcanic eruptions are an important natural cause of climate change on many timescales. A new capability to predict the climatic response to a large tropical eruption for the succeeding 2 years will prove valuable to society. In addition, to detect and attribute anthropogenic influences on climate, including effects of greenhouse gases, aerosols, and ozone-depleting chemicals, it is crucial to quantify the natural fluctuations so as to separate them from anthropogenic fluctuations in the climate record. Studying the responses of climate to volcanic eruptions also helps us to better understand important radiative and dynamical processes that respond in the climate system to both natural and anthropogenic forcings. Furthermore, modeling the effects of volcanic eruptions helps us to improve climate models that are needed to study anthropogenic effects. Large volcanic eruptions inject sulfur gases into the stratosphere, which convert to sulfate aerosols with an e-folding residence time of about 1 year. Large ash particles fall out much quicker. The radiative and chemical effects of this aerosol cloud produce responses in the climate system. By scattering some solar radiation back to space, the aerosols cool the surface, but by absorbing both solar and terrestrial radiation, the aerosol layer heats the stratosphere. For a tropical eruption this heating is larger in the tropics than in the high latitudes, producing an enhanced pole-to-equator temperature gradient, especially in winter. In the Northern Hemisphere winter this enhanced gradient produces a stronger polar vortex, and this stronger jet stream produces a characteristic stationary wave pattern of tropospheric circulation, resulting in winter warming of Northern Hemisphere continents. This indirect advective effect on temperature is stronger than the radiative cooling effect that dominates at lower latitudes and in the summer. The volcanic aerosols also serve as surfaces for heterogeneous chemical reactions that destroy stratospheric ozone, which lowers ultraviolet absorption and reduces the radiative heating in the lower stratosphere, but the net effect is still heating. Because this chemical effect depends on the presence of anthropogenic chlorine, it has only become important in recent decades. For a few days after an eruption the amplitude of the diurnal cycle of surface air temperature is reduced under the cloud. On a much longer timescale, volcanic effects played a large role in interdecadal climate change of the Little Ice Age. There is no perfect index of past volcanism, but more ice cores from Greenland and Antarctica will improve the record. There is no evidence that volcanic eruptions produce El Niño events, but the climatic effects of El Niño and volcanic eruptions must be separated to understand the climatic response to each.