nils-herrmann/clean_scirepeval huggingface dataset:
Impact of stemming on Arabic text summarization

Stemming is a process of reducing inflected words to their stem or root from a generally written word form. This process is used in many text mining application as a feature selection technique. Moreover, Arabic text summarization has increasingly become an important task in natural language processing area (NLP). Therefore, the aim of this paper is to evaluate the impact of three different Arabic stemmers (i.e. Khoja, Larekey and Alkhalil's stemmer) on the text summarization performance for Arabic language. The evaluation of the proposed system, with the three different stemmers and without stemming, on the dataset used shows that the best performance was achieved by Khoja stemmer in term of recall, precision and F1-measure. The evaluation also shows that the performances of the proposed system are significantly improved by applying the stemming process in the pre-processing stage.
Short-term treatment with cabergoline can lead to tumor shrinkage in patients with nonfunctioning pituitary adenomas

This study was carried out to evaluate the effectiveness of cabergoline in the treatment of nonfunctioning pituitary adenomas (NFPA), in a short-term follow-up period. Nineteen patients (10 men and 9 women) followed at the University Hospital of Brasilia and harboring nonfunctioning pituitary macroadenomas were enrolled in the study. Eleven patients were previously submitted to transsphenoidal surgery, and in 8 patients no previous treatment had been instituted. Their response to the use of cabergoline (2 mg/week) by 6 months was evaluated. Significant tumor shrinkage (above 25 % from baseline tumor volume) was observed in 6 (31.6 %) of the 19 patients, and no adverse effects were observed during treatment. In 9 patients (47.4 %), a reduction in tumor volume of at least 10 % was noted, whereas tumor growth was observed in four patients (increase above 25 % was only observed in one patient). Cabergoline (2 mg/week) can lead to significant tumor shrinkage in NFPA in a considerable number of patients, and this effect can be observed early (6 months after starting medication). Thus, this therapeutic strategy may be a low cost and safe alternative for treatment of NFPA in patients with remnant or recurrent tumor after transsphenoidal surgery or in those not operated by contraindications or refusal to surgical procedure.
Cardiac and extracardiac cytoprotective effects of GHRP 6 peptide

The growth hormone-releasing peptide 6 (GHRP6) is a synthetic, six-aminoacid peptide, with the sequence His-D-Trp-Ala-Trp-D-Phe-Lys-NH2. It was originally described as an intestinal Met-enkephalin-derived agent that strongly stimulates the growth hormone release in different mammalian species, also in humans [1, 2]. The GHRP6 and its synthetic analogs are currently used for the clinical diagnosis of different forms of dwarfism . Up to date, its described cytoprotective effects were related to the prevention of cardiac ischemic dysfunction [4-6] and to its hormonal axis-mediated neuroprotective actions [7]. However, current knowledge on GHRP6 and analogous peptides points towards different target structures and different intracellular signaling pathways, and their specific combination depending on the biological context where the given peptide is evaluated . This is the only plausible explanation for the versatile pharmacological effects recently described for these molecules, including the present report. The aim of the present work was to study the potential cytoprotective pharmacological effect of GHRP6 hexapeptide as an anti-necrotic and antiapoptotic cell death agent in cardiac and extracardiac tissues subjected to different lethal insults. For this purpose, a series of versatile in vivo tissular damage models were implemented, induced by ischemia/ reperfusion (I/R) episodes and the administration of a cytotoxic agent. In the case of I/R damage, the molecule was formerly evaluated for cytopro-tection in a provoked acute myocardial infarction (AMI) scenario in pigs, being further evaluated in a multiple organ damage (MOD) model started in rats by a prolonged I/R episode in the liver, and finally in a dilated myocardiopathy (DMC) model induced in rats by the prolonged administration of doxorubicin (DX). The main results obtained are briefly described in the following sections.
Online multi-parameter estimation of interior permanent magnet motor drives with finite control set model predictive control

This study presents an online multiparameter estimation scheme for interior permanent magnet motor drives that exploits the switching ripple of finite control set (FCS) model predictive control (MPC). The combinations consist of two, three, and four parameters are analysed for observability at different operating states. Most of the combinations are rank deficient without persistent excitation (PE) of the system, e.g. by signal injection. This study shows that high frequency current ripples by MPC with FCS are sufficient to create PE in the system. This study also analyses parameter coupling in estimation that results in wrong convergence and propose a decoupling technique. The observability conditions for all the combinations are experimentally validated. Finally, a full parameter estimation along with the decoupling technique is tested at different operating conditions.
A Randomized Trial of High-Flow Oxygen Therapy in Infants with Bronchiolitis.

High-flow oxygen therapy through a nasal cannula has been increasingly used in infants with bronchiolitis, despite limited high-quality evidence of its efficacy. The efficacy of high-flow oxygen therapy through a nasal cannula in settings other than intensive care units (ICUs) is unclear.



In this multicenter, randomized, controlled trial, we assigned infants younger than 12 months of age who had bronchiolitis and a need for supplemental oxygen therapy to receive either high-flow oxygen therapy (high-flow group) or standard oxygen therapy (standard-therapy group). Infants in the standard-therapy group could receive rescue high-flow oxygen therapy if their condition met criteria for treatment failure. The primary outcome was escalation of care due to treatment failure (defined as meeting ≥3 of 4 clinical criteria: persistent tachycardia, tachypnea, hypoxemia, and medical review triggered by a hospital early-warning tool). Secondary outcomes included duration of hospital stay, duration of oxygen therapy, and rates of transfer to a tertiary hospital, ICU admission, intubation, and adverse events.



The analyses included 1472 patients. The percentage of infants receiving escalation of care was 12% (87 of 739 infants) in the high-flow group, as compared with 23% (167 of 733) in the standard-therapy group (risk difference, -11 percentage points; 95% confidence interval, -15 to -7; P<0.001). No significant differences were observed in the duration of hospital stay or the duration of oxygen therapy. In each group, one case of pneumothorax (<1% of infants) occurred. Among the 167 infants in the standard-therapy group who had treatment failure, 102 (61%) had a response to high-flow rescue therapy.



Among infants with bronchiolitis who were treated outside an ICU, those who received high-flow oxygen therapy had significantly lower rates of escalation of care due to treatment failure than those in the group that received standard oxygen therapy. (Funded by the National Health and Medical Research Council and others; Australian and New Zealand Clinical Trials Registry number, ACTRN12613000388718 .).
Comparison of 802 . 11 af and 802 . 22 standards – physical layer and cognitive functionality

This article discusses two standards operating on principles of cognitive radio in television white space (TV WS) frequencies 802.22and 802.11af. The comparative analysis of these systems will be presented and the similarities as well as the differences among these two perspective standards will be discussed from the point of view of physical (PHY), medium access control (MAC) and cognitive layers.
Music Source Separation Using Stacked Hourglass Networks

In this paper, we propose a simple yet effective method for multiple music source separation using convolutional neural networks. Stacked hourglass network, which was originally designed for human pose estimation in natural images, is applied to a music source separation task. The network learns features from a spectrogram image across multiple scales and generates masks for each music source. The estimated mask is refined as it passes over stacked hourglass modules. The proposed framework is able to separate multiple music sources using a single network. Experimental results on MIR-1K and DSD100 datasets validate that the proposed method achieves competitive results comparable to the state-of-the-art methods in multiple music source separation and singing voice separation tasks.
Vaginal polyp in a newborn

Vaginal fibroepithelial polyp is a rare lesion, and although benign, it can be confused with malignant connective tissue lesions. Treatment is simple excision, and recurrence is extremely uncommon. We report a case of a newborn with vaginal fibroepithelial polyp. The authors suggest that vaginal polyp must be considered in the evaluation of interlabial masses in prepubertal girls.
Administration of Pure Ergothioneine to Healthy Human Subjects: Uptake, Metabolism, and Effects on Biomarkers of Oxidative Damage and Inflammation.

We investigated the uptake and pharmacokinetics of l-ergothioneine (ET), a dietary thione with free radical scavenging and cytoprotective capabilities, after oral administration to humans, and its effect on biomarkers of oxidative damage and inflammation.



After oral administration, ET is avidly absorbed and retained by the body with significant elevations in plasma and whole blood concentrations, and relatively low urinary excretion (<4% of administered ET). ET levels in whole blood were highly correlated to levels of hercynine and S-methyl-ergothioneine, suggesting that they may be metabolites. After ET administration, some decreasing trends were seen in biomarkers of oxidative damage and inflammation, including allantoin (urate oxidation), 8-hydroxy-2'-deoxyguanosine (DNA damage), 8-iso-PGF2α (lipid peroxidation), protein carbonylation, and C-reactive protein. However, most of the changes were non-significant.



This is the first study investigating the administration of pure ET to healthy human volunteers and monitoring its uptake and pharmacokinetics. This compound is rapidly gaining attention due to its unique properties, and this study lays the foundation for future human studies.



The uptake and retention of ET by the body suggests an important physiological function. The decreasing trend of oxidative damage biomarkers is consistent with animal studies suggesting that ET may function as a major antioxidant but perhaps only under conditions of oxidative stress.
Duelist Algorithm: An Algorithm Inspired by How Duelist Improve Their Capabilities in a Duel

This paper proposes an optimization algorithm based on how human fight and learn from each duelist. Since this algorithm is based on population, the proposed algorithm starts with an initial set of duelists. The duel is to determine the winner and loser. The loser learns from the winner, while the winner try their new skill or technique that may improve their fighting capabilities. A few duelists with highest fighting capabilities are called as champion. The champion train a new duelists such as their capabilities. The new duelist will join the tournament as a representative of each champion. All duelist are re-evaluated, and the duelists with worst fighting capabilities is eliminated to maintain the amount of duelists. Two optimization problem is applied for the proposed algorithm, together with genetic algorithm, particle swarm optimization and imperialist competitive algorithm. The results show that the proposed algorithm is able to find the better global optimum and faster iteration. Optimization; global, algorithm; duelist;
Mechanisms of suicidal erythrocyte death.

Erythrocyte injury such as osmotic shock, oxidative stress or energy depletion stimulates the formation of prostaglandin E2 through activation of cyclooxygenase which in turn activates a Ca2+ permeable cation channel. Increasing cytosolic Ca2+ concentrations activate Ca2+ sensitive K+ channels leading to hyperpolarization, subsequent loss of KCl and (further) cell shrinkage. Ca2+ further stimulates a scramblase shifting phosphatidylserine from the inner to the outer cell membrane. The scramblase is sensitized for the effects of Ca2+ by ceramide which is formed by a sphingomyelinase following several stressors including osmotic shock. The sphingomyelinase is activated by platelet activating factor PAF which is released by activation of phospholipase A2. Phosphatidylserine at the erythrocyte surface is recognised by macrophages which engulf and degrade the affected cells. Moreover, phosphatidylserine exposing erythrocytes may adhere to the vascular wall and thus interfere with microcirculation. Erythrocyte shrinkage and phosphatidylserine exposure ('eryptosis') mimic features of apoptosis in nucleated cells which however, involves several mechanisms lacking in erythrocytes. In kidney medulla, exposure time is usually too short to induce eryptosis despite high osmolarity. Beyond that high Cl- concentrations inhibit the cation channel and high urea concentrations the sphingomyelinase. Eryptosis is inhibited by erythropoietin which thus extends the life span of circulating erythrocytes. Several conditions trigger premature eryptosis thus favouring the development of anemia. On the other hand, eryptosis may be a mechanism of defective erythrocytes to escape hemolysis. Beyond their significance for erythrocyte survival and death the mechanisms involved in 'eryptosis' may similarly contribute to apoptosis of nucleated cells.
Learning to Compose Skills

We present a differentiable framework capable of learning a wide variety of compositions of simple policies that we call skills. By recursively composing skills with themselves, we can create hierarchies that display complex behavior. Skill networks are trained to generate skill-state embeddings that are provided as inputs to a trainable composition function, which in turn outputs a policy for the overall task. Our experiments on an environment consisting of multiple collect and evade tasks show that this architecture is able to quickly build complex skills from simpler ones. Furthermore, the learned composition function displays some transfer to unseen combinations of skills, allowing for zero-shot generalizations.
Fish immune responses to parasitic copepod (namely sea lice) infection.

Parasitic copepods, in particular sea lice, have considerable impacts upon global freshwater and marine fisheries, with major economic consequences recognized primarily in aquaculture. Sea lice have been a contentious issue with regards to interactions between farmed and wild populations of fish, in particular salmonids, and their potential for detrimental effects at a population level. The following discussion will pertain to aquatic parasitic copepod species for which we have significant information on the host-parasite interaction and host response to infection (Orders Cyclopoida, Poecilostomatoida and Siphonostomatoida). This review evaluates prior research in terms of contributions to understanding parasite stage specific responses by the host, and in many cases draws upon model organisms like Lepeophtheirus salmonis and Atlantic salmon to convey important concepts in fish responses to parasitic copepod infection. The article discusses TH1 and TH2-like host responses in light of parasite immunomodulation of the host, current methods of immunological stimulation and where the current and future work in this field is heading.
Padoc: Enabling social networking in proximity

Apps supporting social networking in proximity are gaining momentum as they enable to both augment face-to-face interaction with a digital channel (e.g. classroom interaction systems) and augment digital interaction by providing a local real life feeling to it (e.g. nearby friends app in Facebook). Such apps effectively provide a cyber-physical space interweaving digital and face-to-face interaction. Currently such applications are mainly relying on Internet connection to the cloud, which makes them inaccessible in parts of the world with scarce Internet connection. Since many of their interactions happen locally, they could theoretically rely on Mobile Networking in Proximity (MNP), where data could be exchanged among devices without the need to rely on the availability of an Internet connection. Unfortunately, there is a lack of off-the-shelf programing support for MNP. This paper addresses this issue and presents Padoc, a middleware for social networking in proximity that provides multi-hop MNP support when cloud connection is unavailable. Furthermore the paper evaluates three MNP message diffusion strategies and presents Heya a novel classroom interaction app running on iOS devices as a proof-of-concept built on top of Padoc.
GPS and SMS-Based Child Tracking System Using Smart Phone

Recently many cases of missing children between ages 14 and 17 years are reported. Parents always worry about the possibility of kidnapping of their children. This paper proposes an Android based solution to aid parents to track their children in real time. Nowadays, most mobile phones are equipped with location services capabilities allowing us to get the device’s geographic position in real time. The proposed solution takes the advantage of the location services provided by mobile phone since most of kids carry mobile phones. The mobile application use the GPS and SMS services found in Android mobile phones. It allows the parent to get their child’s location on a real time map. The system consists of two sides, child side and parent side. A parent’s device main duty is to send a request location SMS to the child’s device to get the location of the child. On the other hand, the child’s device main responsibility is to reply the GPS position to the parent’s device upon request. Child Tracking System, Global Positioning System (GPS), SMS-based Mobile Application.
Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features

We developed and evaluated a multimodal affect detector that combines conversational cues, gross body language, and facial features. The multimodal affect detector uses feature-level fusion to combine the sensory channels and linear discriminant analyses to discriminate between naturally occurring experiences of boredom, engagement/flow, confusion, frustration, delight, and neutral. Training and validation data for the affect detector were collected in a study where 28 learners completed a 32- min. tutorial session with AutoTutor, an intelligent tutoring system with conversational dialogue. Classification results supported a channel × judgment type interaction, where the face was the most diagnostic channel for spontaneous affect judgments (i.e., at any time in the tutorial session), while conversational cues were superior for fixed judgments (i.e., every 20 s in the session). The analyses also indicated that the accuracy of the multichannel model (face, dialogue, and posture) was statistically higher than the best single-channel model for the fixed but not spontaneous affect expressions. However, multichannel models reduced the discrepancy (i.e., variance in the precision of the different emotions) of the discriminant models for both judgment types. The results also indicated that the combination of channels yielded superadditive effects for some affective states, but additive, redundant, and inhibitory effects for others. We explore the structure of the multimodal linear discriminant models and discuss the implications of some of our major findings.
Exploring the Use of Text Classification in the Legal Domain

In this paper, we investigate the application of text classification methods to support law professionals. We present several experiments applying machine learning techniques to predict with high accuracy the ruling of the French Supreme Court and the law area to which a case belongs to. We also investigate the influence of the time period in which a ruling was made on the form of the case description and the extent to which we need to mask information in a full case ruling to automatically obtain training and test data that resembles case descriptions. We developed a mean probability ensemble system combining the output of multiple SVM classifiers. We report results of 98% average F1 score in predicting a case ruling, 96% F1 score for predicting the law area of a case, and 87.07% F1 score on estimating the date of a ruling.
X-band, 17 Watt, solid-state power amplifier for space applications

A new 17-Watt, 8.4 GHz, solid state power amplifier (SSPA) has been developed for the Jet Propulsion Laboratory's Mars Exploration Rover mission. The SSPA consists of a power amplifier microwave module and a highly efficient DC-DC power converter module integrated into a compact package that can be installed near the spacecraft antenna to minimize downlink transmission loss. The SSPA output power is 17 Watts nominal with an input DC power of 59 Watts and nominal input signal of +1 dBm. The unit is qualified to operate over a temperature range of -40/spl deg/C to +70/spl deg/C in vacuum or Martian atmosphere.
APPLYING DATA MINING TECHNIQUES ON ACADAMIC INSTITUTIONAL SYSTEM USING WEKA S

These days, educational institutions and organizations are generating huge amount of data, more than the people can read in their lifetime. It is not possible for a person to learn, understand, decode, and interpret to find valuable information. Data mining is one of the most popular method which can be used to identify hidden patterns from large databases. User can extract historical, hidden details, and previously unknown information, from large repositories by applying required mining techniques. There are two algorithms which can be used to classify and predict, such as supervised learning and unsupervised learning. Classification is a technique which performs an induction on current data (existing data) and predicts future class. The main objective of classification is to make an unknown class to known class by consulting its neighbor class. therefore it is called as supervised learning, it builds the classifier by consulting with the known class labels such as k-nearest neighbor algorithm (k-NN), Naïve Bayes (NB), support vector machine (SVM), decision tree. Clustering is an unsupervised learning that builds a model to group similar objects into categories without consulting a class label. The main objective of clustering is find the distance between objects like nearby and faraway based on their similarities and dissimilarities it groups the objects and detects outliers. In this paper Weka tool is used to analyze by applying preprocessing, classification on institutional academic result of under graduate students of computer science & engineering.  Weka, classifier, supervised learning,