nils-herrmann/clean_scirepeval huggingface dataset:
GermEval 2014 Named Entity Recognition Shared Task :

This paper describes the GermEval 2014 Named Entity Recognition (NER) Shared Task workshop at KONVENS. It provides background information on the motivation of this task, the data-set, the evaluation method, and an overview of the participating systems, followed by a discussion of their results. In contrast to previous NER tasks, the GermEval 2014 edition uses an extended tagset to account for derivatives of names and tokens that contain name parts. Further, nested named entities had to be predicted, i.e. names that contain other names. The eleven participating teams employed a wide range of techniques in their systems. The most successful systems used state-of-theart machine learning methods, combined with some knowledge-based features in hybrid systems.
Cyberbullying, school bullying, and psychological distress: a regional census of high school students.

Using data from a regional census of high school students, we have documented the prevalence of cyberbullying and school bullying victimization and their associations with psychological distress.



In the fall of 2008, 20,406 ninth- through twelfth-grade students in MetroWest Massachusetts completed surveys assessing their bullying victimization and psychological distress, including depressive symptoms, self-injury, and suicidality.



A total of 15.8% of students reported cyberbullying and 25.9% reported school bullying in the past 12 months. A majority (59.7%) of cyberbullying victims were also school bullying victims; 36.3% of school bullying victims were also cyberbullying victims. Victimization was higher among nonheterosexually identified youths. Victims report lower school performance and school attachment. Controlled analyses indicated that distress was highest among victims of both cyberbullying and school bullying (adjusted odds ratios [AORs] were from 4.38 for depressive symptoms to 5.35 for suicide attempts requiring medical treatment). Victims of either form of bullying alone also reported elevated levels of distress.



Our findings confirm the need for prevention efforts that address both forms of bullying and their relation to school performance and mental health.
Laser Engineered Net Shaping of Nickel-Based Superalloy Inconel 718 Powders onto AISI 4140 Alloy Steel Substrates: Interface Bond and Fracture Failure Mechanism

As a prospective candidate material for surface coating and repair applications, nickel-based superalloy Inconel 718 (IN718) was deposited on American Iron and Steel Institute (AISI) 4140 alloy steel substrate by laser engineered net shaping (LENS) to investigate the compatibility between two dissimilar materials with a focus on interface bonding and fracture behavior of the hybrid specimens. The results show that the interface between the two dissimilar materials exhibits good metallurgical bonding. Through the tensile test, all the fractures occurred in the as-deposited IN718 section rather than the interface or the substrate, implying that the as-deposited interlayer bond strength is weaker than the interfacial bond strength. From the fractography using scanning electron microscopy (SEM) and energy disperse X-ray spectrometry (EDS), three major factors affecting the tensile fracture failure of the as-deposited part are (i) metallurgical defects such as incompletely melted powder particles, lack-of-fusion porosity, and micropores; (ii) elemental segregation and Laves phase, and (iii) oxide formation. The fracture failure mechanism is a combination of all these factors which are detrimental to the mechanical properties and structural integrity by causing premature fracture failure of the as-deposited IN718.
The Hunt for Natural Skin Whitening Agents

Skin whitening products are commercially available for cosmetic purposes in order to obtain a lighter skin appearance. They are also utilized for clinical treatment of pigmentary disorders such as melasma or postinflammatory hyperpigmentation. Whitening agents act at various levels of melanin production in the skin. Many of them are known as competitive inhibitors of tyrosinase, the key enzyme in melanogenesis. Others inhibit the maturation of this enzyme or the transport of pigment granules (melanosomes) from melanocytes to surrounding keratinocytes. In this review we present an overview of (natural) whitening products that may decrease skin pigmentation by their interference with the pigmentary processes.
Online shopping post-payment dissonance: Dissonance reduction strategy using online consumer social experiences

This study applied the concept of online consumer social experiences (OCSEs) to reduce online shopping post-payment dissonance (i.e., dissonance occurring between online payment and product receipt). Two types of OCSEs were developed: indirect social experiences (IDSEs) and virtual social experiences (VSEs). Two studies were conducted, in which 447 college students were enrolled. Study 1 compared the effects of OCSEs and non-OCSEs when online shopping post-payment dissonance occurred. The results indicate that providing consumers affected by online shopping post-payment dissonance with OCSEs reduces dissonance and produces higher satisfaction, higher repurchase intention, and lower complaint intention than when no OCSEs are provided. In addition, consumers’ interpersonal trust (IPT) and susceptibility to interpersonal informational influence (SIII) moderated the positive effects of OCSEs. Study 2 compared the effects of IDSEs and VSEs when online shopping post-payment dissonance occurred. The results sugomputing need for control omputer-mediated communication pprehension gest that the effects of IDSEs and VSEs on satisfaction, repurchase intention, and complaint intention are moderated by consumers’ computing need for control (CNC) and computer-mediated communication apprehension (CMCA). The consumers with high CNC and low CMCA preferred VSEs, whereas the consumers with low CNC and high CMCA preferred IDSEs. The effects of VSEs and IDSEs on consumers with high CNC and CMCA and those with low CNC and CMCA were not significantly different.
Doing Q methodology : theory , method and interpretation

This paper has a marked practical aspect. We wish to encourage and facilitate the use of Q methodology amongst psychologists interested in qualitative research. The paper duly answers a number of pertinent ‘how to do Q’ questions. Yet our primary intention is not to produce an exhaustive ‘how to do Q’ guide. In discussing issues of theory, method and interpretation in Q methodology, the main aim of the paper is rather to address some of the more common misunderstandings and misrepresentations that constitute obstacles to the use of one of the very first ‘alternative’ methods to have been developed in the context of psychology. In addressing such obstacles, the paper hopes to bring ‘Why do Q?’ questions to the fore. In so doing, Q methodology will also be ‘positioned’ in relation to a number of other qualitative research methods, each of which currently enjoys a degree of prominence within the psychological discipline.  Research in Psychology 2005; 2: 67!/91
Hospital Engineering of Medical Devices in France

Biomedical engineering handles the organization and functioning of medical devices in the hospital. This is a strategic function of the hospital for its balance, development, and growth. This is a major focus in internal and external reports of the hospital. It's based on piloting of medical devices needs and the procedures of biomedical teams’ intervention. Multi-year projects of capital and operating expenditure in medical devices are planned as coherently as possible with the hospital's financial budgets. An information system is an essential tool for monitoring medical devices engineering and relationship with medical services.
On the Limitations of Unsupervised Bilingual Dictionary Induction

Unsupervised machine translation—i.e., not assuming any cross-lingual supervision signal, whether a dictionary, translations, or comparable corpora—seems impossible, but nevertheless, Lample et al. (2018a) recently proposed a fully unsupervised machine translation (MT) model. The model relies heavily on an adversarial, unsupervised alignment of word embedding spaces for bilingual dictionary induction (Conneau et al., 2018), which we examine here. Our results identify the limitations of current unsupervised MT: unsupervised bilingual dictionary induction performs much worse on morphologically rich languages that are not dependent marking, when monolingual corpora from different domains or different embedding algorithms are used. We show that a simple trick, exploiting a weak supervision signal from identical words, enables more robust induction, and establish a near-perfect correlation between unsupervised bilingual dictionary induction performance and a previously unexplored graph similarity metric.
All You Need is: Evading Hate Speech Detection

With the spread of social networks and their unfortunate use for hate speech, automatic detection of the latter has become a pressing problem. In this paper, we reproduce seven state-of-the-art hate speech detection models from prior work, and show that they perform well only when tested on the same type of data they were trained on. Based on these results, we argue that for successful hate speech detection, model architecture is less important than the type of data and labeling criteria. We further show that all proposed detection techniques are brittle against adversaries who can (automatically) insert typos, change word boundaries or add innocuous words to the original hate speech. A combination of these methods is also effective against Google Perspective - a cutting-edge solution from industry. Our experiments demonstrate that adversarial training does not completely mitigate the attacks, and using character-level features makes the models systematically more attack-resistant than using word-level features.
Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering

Clustering is among the most fundamental tasks in machine learning and artificial intelligence. In this paper, we propose Variational Deep Embedding (VaDE), a novel unsupervised generative clustering approach within the framework of Variational Auto-Encoder (VAE). Specifically, VaDE models the data generative procedure with a Gaussian Mixture Model (GMM) and a deep neural network (DNN): 1) the GMM picks a cluster; 2) from which a latent embedding is generated; 3) then the DNN decodes the latent embedding into an observable. Inference in VaDE is done in a variational way: a different DNN is used to encode observables to latent embeddings, so that the evidence lower bound (ELBO) can be optimized using the Stochastic Gradient Variational Bayes (SGVB) estimator and the reparameterization trick. Quantitative comparisons with strong baselines are included in this paper, and experimental results show that VaDE significantly outperforms the stateof-the-art clustering methods on 5 benchmarks from various modalities. Moreover, by VaDE’s generative nature, we show its capability of generating highly realistic samples for any specified cluster, without using supervised information during training.
Adaptive Neural Network Control of Shunt Active Filter

This paper presents a new control design of shunt active filter for harmonic compensation and power factor correction in a power distribution system. The topology of the shunt active filter is based on a 3-phase hysteresis control voltage source inverter (VSI) and a control circuit. The control circuit using two neural network controllers is proposed. One adaptive neural network (NN) controller is designed to estimate the harmonic components of the distorted load current and the reactive component of the fundamental component of the distorted load current. Another neural network controller that uses Levenberg Marquardt back propagation (LMBP) for its training is designed to generate accurate switch control signals for the hysteresis control VSI. A power factor correction function is incorporated in the shunt active filter to achieve a power factor that is near to unity. The shunt active filter with neural network control can significantly reduce harmonic distortion and the reactive component in the load current and can adapt to variations in system operating conditions. The strategy has been extracting the instantaneous value of the reactive fundamental component and harmonic components from the load current by means of this neural network (NN) topology. A previous training with waveforms distorted by harmonics up to the 29th order is showed to be enough for a high accuracy, working with typical nonlinear loads. By using these reactive fundamental and harmonic components as the shunt APF reference distortion signal is obtained. Simulation results with the MATLAB Simulink. The simulation study results of the new shunt active filter identification technique compared to other similar methods are found quite satisfactory by assuring good filtering characteristics and high system stability. Index terms – Active filters, Active power line conditioners, Adaptive control, Neural network, Power quality.
RECEPTIVE FIELDS AND FUNCTIONAL ARCHITECTURE IN TWO NONSTRIATE VISUAL AREAS (18 AND 19) OF THE CAT.

To UNDERSTAND VISION in physiological terms represents a formidable problem for the biologist. I t am0 unts to learning how the nervous system handles incoming messages so that form, color, movement, and depth can be perceived and interpreted. One approach, perhaps the most direct, is to stimulate the retina with patterns of light while recording from single cells or fibers at various points along the visual pa thway. For each cell the optimum stimulus can be determined, and one can note the charac teristics common to cells at the next. each level in the visual pathway, and compare a given level with
Preventing Information Leakage from Encoded Data in Lattice Based Cryptography

Lattice based Cryptography is an important sector which is ensuring cloud data security in present world. It provides a stronger belief of security in a way that the average-case of certain problem is akin to the worst-case of those problems. There are strong indications that these problems will remain safe under the availability of quantum computers, unlike the widely used issues like integer-factorization and discrete logarithm upon which most of the typical cryptosystems relies. In this paper, we tend to discuss the security dimension of Lattice based cryptography whose power lies within the hardness of lattice problems. Goldreich-Goldwasser-Halevi (GGH) public-key cryptosystem is an exemplar of lattice-based cryptosystems. Its security depends on the hardness of lattice issues. GGH is easy to understand and is widely used due to its straightforward data encoding and decoding procedures. Phong Nguyen, in his paper showed that there's a significant flaw within the style of the GGH scheme as ciphertext leaks information on the plaintext. Due to this flaw the practical usage of GGH cryptosystem is limiting to some extent. So as to enhance the safety and usefulness of the GGH cryptosystem, in this paper we proposed an improvised GGH encryption and decryption functions which prevented information leakage. We have implemented a package in MATLAB for the improvement of GGH cryptosystem. In our work we proposed some methods to improve GGH algorithm and make it more secure and information leakage resistant.
Describing Robotic Bat Flight with Stable Periodic Orbits

From a dynamic system point of view, bat locomotion stands out among other forms of flight. During a large part of bat wingbeat cycle the moving body is not in a static equilibrium. This is in sharp contrast to what we observe in other simpler forms of flight such as insects, which stay at their static equilibrium. Encouraged by biological examinations that have revealed bats exhibit periodic and stable limit cycles, this work demonstrates that one effective approach to stabilize articulated flying robots with bat morphology is locating feasible limit cycles for these robots; then, designing controllers that retain the closed-loop system trajectories within a bounded neighborhood of the designed periodic orbits. This control design paradigm has been evaluated in practice on a recently developed bio-inspired robot called Bat Bot (B2).
The glycolytic enzyme PKM2 bridges metabolic and inflammatory dysfunction in coronary artery disease

Abnormal glucose metabolism and enhanced oxidative stress accelerate cardiovascular disease, a chronic inflammatory condition causing high morbidity and mortality. Here, we report that in monocytes and macrophages of patients with atherosclerotic coronary artery disease (CAD), overutilization of glucose promotes excessive and prolonged production of the cytokines IL-6 and IL-1β, driving systemic and tissue inflammation. In patient-derived monocytes and macrophages, increased glucose uptake and glycolytic flux fuel the generation of mitochondrial reactive oxygen species, which in turn promote dimerization of the glycolytic enzyme pyruvate kinase M2 (PKM2) and enable its nuclear translocation. Nuclear PKM2 functions as a protein kinase that phosphorylates the transcription factor STAT3, thus boosting IL-6 and IL-1β production. Reducing glycolysis, scavenging superoxide and enforcing PKM2 tetramerization correct the proinflammatory phenotype of CAD macrophages. In essence, PKM2 serves a previously unidentified role as a molecular integrator of metabolic dysfunction, oxidative stress and tissue inflammation and represents a novel therapeutic target in cardiovascular disease.
The cross-talk between autophagy and endoplasmic reticulum stress in blood-spinal cord barrier disruption after spinal cord injury

Spinal cord injury induces the disruption of blood-spinal cord barrier and triggers a complex array of tissue responses, including endoplasmic reticulum (ER) stress and autophagy. However, the roles of ER stress and autophagy in blood-spinal cord barrier disruption have not been discussed in acute spinal cord trauma. In the present study, we respectively detected the roles of ER stress and autophagy in blood-spinal cord barrier disruption after spinal cord injury. Besides, we also detected the cross-talking between autophagy and ER stress both in vivo and in vitro. ER stress inhibitor, 4-phenylbutyric acid, and autophagy inhibitor, chloroquine, were respectively or combinedly administrated in the model of acute spinal cord injury rats. At day 1 after spinal cord injury, blood-spinal cord barrier was disrupted and activation of ER stress and autophagy were involved in the rat model of trauma. Inhibition of ER stress by treating with 4-phenylbutyric acid decreased blood-spinal cord barrier permeability, prevented the loss of tight junction (TJ) proteins and reduced autophagy activation after spinal cord injury. On the contrary, inhibition of autophagy by treating with chloroquine exacerbated blood-spinal cord barrier permeability, promoted the loss of TJ proteins and enhanced ER stress after spinal cord injury. When 4-phenylbutyric acid and chloroquine were combinedly administrated in spinal cord injury rats, chloroquine abolished the blood-spinal cord barrier protective effect of 4-phenylbutyric acid by exacerbating ER stress after spinal cord injury, indicating that the cross-talking between autophagy and ER stress may play a central role on blood-spinal cord barrier integrity in acute spinal cord injury. The present study illustrates that ER stress induced by spinal cord injury plays a detrimental role on blood-spinal cord barrier integrity, on the contrary, autophagy induced by spinal cord injury plays a furthersome role in blood-spinal cord barrier integrity in acute spinal cord injury.
Blockchain Technology as s Support Infrastructure in e-Government

The blockchain technology, including Bitcoin and other crypto currencies, has been adopted in many application areas during recent years. However, the main attention has been on the currency and not so much on the underlying blockchain technology, including peer-to-peer networking, security and consensus mechanisms. This paper argues that we need to look beyond the currency applications and investigate the potential use of the blockchain tech‐ nology in governmental tasks such as digital ID management and secure docu‐ ment handling. The paper discusses the use of blockchain technology as a plat‐ form for various applications in e-Government and furthermore as an emerging support infrastructure by showing that blockchain technology demonstrates a potential for authenticating many types of persistent documents.
Innovative lead discovery strategies for tropical diseases

Lead discovery is currently a key bottleneck in the pipeline for much-needed novel drugs for tropical diseases such as malaria, tuberculosis, African sleeping sickness, leishmaniasis and Chagas disease. Here, we discuss the different approaches to lead discovery for tropical diseases and emphasize a coordination strategy that involves highly integrated partnerships and networks between scientists in academic institutions and industry in both wealthy industrialized countries and disease-endemic countries. This strategy offers the promise of reducing the inherently high attrition rate of the early stages of discovery research, thereby increasing the chances of success and enhancing cost-effectiveness.
Deep learning bank distress from news and numerical financial data

In this paper we focus our attention on the exploitation of the information contained in financial news to enhance the performance of a classifier of bank distress. Such information should be analyzed and inserted into the predictive model in the most efficient way and this task deals with the issues related to text analysis and specifically to the analysis of news media. Among the different models proposed for such purpose, we investigate one of the possible deep learning approaches, based on a doc2vec representation of the textual data, a kind of neural network able to map the sequence of words contained within a text onto a reduced latent semantic space. Afterwards, a second supervised neural network is trained combining news data with standard financial figures to classify banks whether in distressed or tranquil states, based on a small set of known distress events. Then the final aim is not only the improvement of the predictive performance of the classifier but also to assess the importance of news data in the classification process. Does news data really bring more useful information not contained in standard financial variables? Our results seem to confirm such hypothesis.
Impaired wake-promoting mechanisms in ghrelin receptor-deficient mice.

Ghrelin receptors are expressed by key components of the arousal system. Exogenous ghrelin induces behavioral activation, promotes wakefulness and stimulates eating. We hypothesized that ghrelin-sensitive mechanisms play a role in the arousal system. To test this, we investigated the responsiveness of ghrelin receptor knockout (KO) mice to two natural wake-promoting stimuli. Additionally, we assessed the integrity of their homeostatic sleep-promoting system using sleep deprivation. There was no significant difference in the spontaneous sleep-wake activity between ghrelin receptor KO and wild-type (WT) mice. WT mice mounted robust arousal responses to a novel environment and food deprivation. Wakefulness increased for 6 h after cage change accompanied by increases in body temperature and locomotor activity. Ghrelin receptor KO mice completely lacked the wake and body temperature responses to new environment. When subjected to 48 h food deprivation, WT mice showed marked increases in their waking time during the dark periods of both days. Ghrelin receptor KO mice failed to mount an arousal response on the first night and wake increases were attenuated on the second day. The responsiveness to sleep deprivation did not differ between the two genotypes. These results indicate that the ghrelin-receptive mechanisms play an essential role in the function of the arousal system but not in homeostatic sleep-promoting mechanisms.