nils-herrmann/clean_scirepeval huggingface dataset:
The UbiCARS Model-Driven Framework: Automating Development of Recommender Systems for Commerce

Recommendations of products to customers are proved to boost sales, increase customer satisfaction and improve user experience, making recommender systems an important tool for retail businesses. With recent technological advancements in AmI and Ubiquitous Computing, the benefits of recommender systems can be enjoyed not only in e-commerce, but in the physical store scenario as well. However, developing effective context-aware recommender systems by non-expert practitioners is not an easy task due to the complexity of building the necessary data models and selecting and configuring recommendation algorithms. In this paper we apply the Model Driven Development paradigm on the physical commerce recommendation domain by defining a UbiCARS Domain Specific Modelling Language, a modelling editor and a system, that aim to reduce complexity, abstract the technical details and expedite the development and application of State-of-the-Art recommender systems in ubiquitous environments (physical retail stores), as well as to enable practitioners to utilize additional data resulting from ubiquitous user-product interaction in the recommendation process to improve recommendation accuracy.
Regression Trees and Random forest based feature selection for malaria risk exposure prediction

This paper deals with prediction of anopheles number, the main vector of malaria risk, using environmental and climate variables. The variables selection is based on an automatic machine learning method using regression trees, and random forests combined with stratified two levels cross validation. The minimum threshold of variables importance is accessed using the quadratic distance of variables importance while the optimal subset of selected variables is used to perform predictions. Finally the results revealed to be qualitatively better, at the selection, the prediction, and the CPU time point of view than those obtained by GLM-Lasso method.
Randomized, placebo-controlled study of cetirizine and loratadine in children with seasonal allergic rhinitis.

Pharmacologic treatment is a mainstay of allergy therapy and many caregivers use over-the-counter antihistamines for the treatment of seasonal allergic rhinitis (SAR) symptoms in children.



To assess the efficacy and safety of cetirizine 10 mg syrup versus loratadine 10 mg syrup versus placebo syrup in a randomized double-blind study of children, ages 6-11 years, with SAR.



This randomized, double-blind, parallel-group, placebo-controlled study was conducted at 71 U.S. centers during the spring tree and grass pollen season. After a 1-week placebo run-in period, qualified subjects were randomized to once-daily cetirizine 10 mg (n = 231), loratadine 10 mg (n = 221), and placebo (n = 231) for 2 weeks. The primary efficacy end point was change from baseline in the subject's mean reflective total symptom severity complex (TSSC) score over 14 days.



Children treated with cetirizine experienced significantly greater TSSC score reductions versus children treated with placebo over 14 days (least square mean change, -2.1 versus -1.6; p = 0.006). The differences in TSSC score improvement over 14 days between the cetirizine versus loratadine groups (-2.1 versus -1.8; p = 0.124) and between the loratadine versus placebo groups (-1.8 versus -1.6; p = 0.230) were not statistically significant. Predominant adverse events in the cetirizine, loratadine, and placebo groups were headache (3.5, 3.6, and 3.1%, respectively) and pharyngitis (3.5, 2.7, and 3.5%, respectively). Somnolence was reported in three subjects (1.3%) treated with cetirizine and in none of the other subjects.



Cetirizine 10 mg was statistically significantly more efficacious than placebo in the treatment of SAR symptoms in children ages 6-11 years. Symptom improvement was not significantly different between the loratadine 10 mg and placebo groups.
Machine learning methods for fully automatic recognition of facial expressions and facial actions

We present a systematic comparison of machine learning methods applied to the problem of fully automatic recognition of facial expressions. We explored recognition of facial actions from the facial action coding system (FACS), as well as recognition of fall facial expressions. Each video-frame is first scanned in real-time to detect approximately upright frontal faces. The faces found are scaled into image patches of equal size, convolved with a bank of Gabor energy filters, and then passed to a recognition engine that codes facial expressions into 7 dimensions in real time: neutral, anger, disgust, fear, joy, sadness, surprise. We report results on a series of experiments comparing recognition engines, including AdaBoost, support vector machines, linear discriminant analysis, as well as feature selection techniques. Best results were obtained by selecting a subset of Gabor filters using AdaBoost and then training support vector machines on the outputs of the filters selected by AdaBoost. The generalization performance to new subjects for recognition of full facial expressions in a 7-way forced choice was 93% correct, the best performance reported so far on the Cohn-Kanade FACS-coded expression dataset. We also applied the system to fully automated facial action coding. The present system classifies 18 action units, whether they occur singly or in combination with other actions, with a mean agreement rate of 94.5% with human FACS codes in the Cohn-Kanade dataset. The outputs of the classifiers change smoothly as a function of time and thus can be used to measure facial expression dynamics.
hyperdoc2vec: Distributed Representations of Hypertext Documents

Hypertext documents, such as web pages and academic papers, are of great importance in delivering information in our daily life. Although being effective on plain documents, conventional text embedding methods suffer from information loss if directly adapted to hyper-documents. In this paper, we propose a general embedding approach for hyper-documents, namely, hyperdoc2vec, along with four criteria characterizing necessary information that hyper-document embedding models should preserve. Systematic comparisons are conducted between hyperdoc2vec and several competitors on two tasks, i.e., paper classification and citation recommendation, in the academic paper domain. Analyses and experiments both validate the superiority of hyperdoc2vec to other models w.r.t. the four criteria.
Dynamic Modelling of the Compliant KUKA-DLR Lightweight Robot

This paper addresses the development of a model that reproduces the dynamic behaviour of a redundant, 7 degrees of freedom robotic manipulator, namely the Kuka Lightweight Robot IV, in the Robotic Surgery Laboratory of the Instituto Superior Técnico. For this purpose, the control architecture behind the Lightweight Robot (LWR) is presented, as well as, the joint and the Cartesian level impedance control aspects. Then, the manipulator forward and inverse kinematic models are addressed, in which the inverse kinematics relies on the Closed Loop Inverse Kinematic method (CLIK). Redundancy resolution methods are used to ensure that the joint angle values remain bounded considering their physical limits. The joint level model is the first presented, followed by the Cartesian level model. The redundancy inherent to the Cartesian model is compensated by a null space controller, developed by employing the impedance superposition method. Finally, the effect of possible faults occurring in the system are simulated using the derived model.
Growth of a tropical marine yeast Yarrowia lipolytica NCIM 3589 on bromoalkanes: relevance of cell size and cell surface properties.

Yarrowia lipolytica 3589, a tropical marine yeast, grew aerobically on a broad range of bromoalkanes varying in carbon chain length and differing in degree and position of bromide group. Amongst the bromoalkanes studied, viz. 2-bromopropane (2-BP), 1-bromobutane (1-BB), 1,5-dibromopentane (1,5-DBP) and 1-bromodecane (1-BD), the best utilized was 1-BD, with a maximal growth rate (μ(max) ) of 0.055 h⁻¹ and an affinity ratio (μ(max) /K(s) ) of 0.022. Utilization of these bromoalkanes as growth substrates was associated with a concomitant release of bromide (8202.9 µm) and cell mass (36 × 10⁹ cells/ml), occurring maximally on 1-BD. Adherence of yeast cells to these hydrophobic bromoalkanes was observed microscopically, with an increase in cell size and surface hydrophobicity. The maximal cell diameter was for 1-BD (4.66 µm), resulting in an increase in the calculated cell surface area (68.19 µm²) and sedimentation velocity (1.31 µm/s). Cell surface hydrophobicity values by microbial adhesion to solvents (MATS) analysis for yeasts grown on bromoalkanes and glucose were significantly high, i.e. >80%. Similarly, water contact angles also indicate that the cell surface of yeast cells grown in glucose possess a relatively more hydrophilic cell surface (θ = 49.1°), whereas cells grown in 1-BD possess a more hydrophobic cell surface (θ = 90.7°). No significant change in emulsification activity or surface tension was detected in the cell-free supernatant. Thus adherence to the bromoalkane droplets by an increase in cell size and surface hydrophobicity leading to debromination of the substrate might be the strategy employed in bromoalkane utilization and growth by Y. lipolytica 3589.
A 10-MHz isolated class-Φ2 synchronous resonant dc-dc converter

In the multi-MHz low voltage, high current applications, Synchronous Rectification (SR) is strongly needed due to the forward recovery and the high conduction loss of the rectifier diodes. This paper applies the SR technique to a 10-MHz isolated class-Φ2 resonant converter and proposes a self-driven level-shifted Resonant Gate Driver (RGD) for the SR FET. The proposed RGD can reduce the average on-state resistance and the associated conduction loss of the MOSFET. It also provides precise switching timing for the SR so that the body diode conduction time of the SR FET can be minimized. A 10-MHz prototype with 18 V input, 5 V/2 A output was built to verify the advantage of the SR with the proposed RGD. At full load of 2 A, the SR with the proposed RGD improves the converter efficiency from 80.2% using the SR with the conventional RGD to 82% (an improvement of 1.8%). Compared to the efficiency of 77.3% using the diode rectification, the efficiency improvement is 4.7%.
Applying Cellular Automata for Simulating and Assessing Urban Growth Scenario Based in Nairobi, Kenya

This research explores urban growth based scenarios for the city of Nairobi using a cellular automata urban growth model (UGM). African cities have experienced rapid urbanization over the last decade due to increased population growth and high economic activities. We used multi-temporal Landsat imageries for 1976, 1986, 2000 and 2010 to investigate urban land-use changes in Nairobi. Our UGM used data from urban land-use of 1986 and 2010, road data, slope data and exclusion layer. Monte-Carlo technique was used for model calibration and Multi Resolution Validation (MRV) technique for validation. Simulation of urban land-use was done up to the year 2030 when Kenya plans to attain Vision 2030. Three scenarios were explored in the urban modelling process; unmanaged growth with no restriction on environmental areas, managed growth with moderate protection, and a managed growth with maximum protection on forest, agricultural areas, and urban green. Thus alternative scenario development using UGM is useful for planning purposes so as to ensure sustainable development is achieved. UGM provides quantitative, visual, spatial and temporal information which aid policy and decision makers can make informed decisions. Urban Growth; Scenarios; Nairobi; Cellular automata; Simulation; sustainable development
The design and implementation of a low-cost 360-degree color LED display system

This paper is to introduce the system design and implementation of a low-cost 360-degree true-color light emitting diode (LED) display system. This energy-saving LED display system employs both rotating scan scheme and a precision control in combination of time and space to provide three same screen displays of resolution 320×240 with only three columns of tricolor (Red, Green, Blue) LEDs. The frequency of scan and the speed of rotation are high enough to be indistinguishable by human eyes, hence the display appears to be constantly illuminated and the brilliant color image can be seen from any viewing angle. In order to realize colorful images in real time, both LED gradation driving method and display data processing are implemented in CPLD (Complex Programmable Logic Array). The experimental results show that the LED display system has an efficient and satisfying display quality, compared to the traditional LED dot matrix display and commercial surround color LED display.
Multi-city sustainable regional urban growth simulation — MSRUGS : a case study along the midsection of Silk Road of China

Urban growth along the middle section of the ancient silk-road of China (so called West Yellow River Corridor—He-Xi Corridor) has taken a unique path deviating from what is commonly seen in the coastal China. Urban growth here has been driven by historical heritage, transportation connection between East and West China, and mineral exploitation. However, it has been constrained by water shortage and harsh natural environment because this region is located in arid and semi-arid climate zones. This paper attempts to construct a multi-city agent-based model to explore possible trajectories of regional urban growth along the entire He-Xi Corridor under a severe environment risk, over urban growth under an extreme threat of water shortage. In contrast with current ABM approaches, our model will simulate urban growth in a large administrative region consisting of a system of cities. It simultaneously considers the spatial variations of these cities in terms of population size, development history, water resource endowment and sustainable development potential. It also explores potential impacts of exogenous inter-city interactions on future urban growth on the basis of urban gravity model. The algorithmic foundations of three types of agents, developers, conservationists and regional-planners, are discussed. Simulations with regard to three different development scenarios are presented and analyzed.
Prior and contextual emotion of words in sentential context

A set of words labeled with their prior emotion is an obvious place to start on the automatic discovery of the emotion of a sentence, but it is clear that context must also be considered. It may be that no simple function of the labels on the individual words captures the overall emotion of the sentence; words are interrelated and they mutually influence their affect-related interpretation. It happens quite often that a word which invokes emotion appears in a neutral sentence, or that a sentence with no emotional word carries an emotion. This could also happen among different emotion classes. The goal of this work is to distinguish automatically between prior and contextual emotion, with a focus on exploring features important in this task. We present a set of features which enable us to take the contextual emotion of a word and the syntactic structure of the sentence into account to put sentences into emotion classes. The evaluation includes assessing the performance of different feature sets across multiple classification methods. We show the features and a promising learning method which significantly outperforms two reasonable baselines. We group our features by the similarity of their nature. That is why another facet of our evaluation is to consider each group of the features separately and investigate how well they contribute to the result. The experiments show that all features contribute to the result, but it is the combination of all the features that gives the best performance.
Neurogenic lower urinary tract dysfunction.

The two roles of the lower urinary tract are storage of urine and emptying at appropriate times. Optimal and coordinated activity of the bladder and urethra is subject to complex neural control which involves all levels of the nervous system, from cortex to peripheral nerve. This explains the high prevalence of urinary disturbances in neurological disease. Information obtained from history taking and supplemented by use of a bladder diary forms the cornerstone of evaluation. Ultrasonography is used to assess the degree of incomplete bladder emptying, and for assessing the upper tracts. Urodynamic tests, with or without simultaneous fluoroscopic monitoring, assess detrusor and bladder outlet function and give fundamental information about detrusor pressure and thus the risk of upper tract damage. Impaired emptying is most often managed by clean-intermittent self-catheterization, which should be initiated if the postvoid residual urine exceeds one-third of bladder capacity or is greater than 100mL, or rarely if spontaneous voiding is dangerous due to high detrusor pressure. Storage symptoms are most often managed using antimuscarinic medications. Intradetrusor injection of botulinum toxin type A is emerging as an effective treatment for managing detrusor overactivity. Understanding of the underlying mechanism of lower urinary tract dysfunction is crucial for effective management.
Occupational Stress and Burnout among Surgeons in Fiji

This study examined the levels of occupational stress and burnout among surgeons in Fiji.



A document set comprising a cover letter; a consent form; a sociodemographic and supplementary information questionnaire; the Maslach Burnout Inventory (MBI); the 12-item General Health Questionnaire (GHQ-12); the Alcohol Use Disorders Identification Test (AUDIT); and the Professional Quality of Life (ProQOL) questionnaires were provided to surgeons from three public divisional hospitals in Fiji. Thirty-six of 43 (83.7%) invited surgeons participated in the study.



According to their MBI scores, surgeons suffered from low (10, 27.8%), moderate (23, 63.9%), and high (3, 8.3%) levels of burnout. Comparatively, 23 (63.9%) demonstrated moderate burnout according to their ProQOL scores. Substantial psychiatric morbidity was observed in 16 (44.0%) surgeons per their GHQ-12 scores. Consumption of alcohol was noted in 29 (80.6%) surgeons, and 12 (33.4%) had AUDIT scores characterizing their alcohol use in excess of low-risk guidelines or as harmful or hazardous drinking. Surgeons of Fijian nationality showed higher MBI emotional exhaustion and depersonalization scores compared with surgeons of other nationalities. Surgeons with an awareness of the availability of counseling services at their hospitals showed low AUDIT and ProQOL burnout scores. Smokers, alcohol drinkers, and kava drinkers showed higher AUDIT scores.



This study highlights a level of occupational stress and burnout among surgeons in Fiji and a lack of awareness of their mental and physical well-being. The authors recommend that occupational stress and burnout intervention strategies be put in place in hospitals in Fiji.
Activity of D-amino acid oxidase is widespread in the human central nervous system

It has been proposed that D-amino acid oxidase (DAO) plays an essential role in degrading D-serine, an endogenous coagonist of N-methyl-D-aspartate (NMDA) glutamate receptors. DAO shows genetic association with amyotrophic lateral sclerosis (ALS) and schizophrenia, in whose pathophysiology aberrant metabolism of D-serine is implicated. Although the pathology of both essentially involves the forebrain, in rodents, enzymatic activity of DAO is hindbrain-shifted and absent in the region. Here, we show activity-based distribution of DAO in the central nervous system (CNS) of humans compared with that of mice. DAO activity in humans was generally higher than that in mice. In the human forebrain, DAO activity was distributed in the subcortical white matter and the posterior limb of internal capsule, while it was almost undetectable in those areas in mice. In the lower brain centers, DAO activity was detected in the gray and white matters in a coordinated fashion in both humans and mice. In humans, DAO activity was prominent along the corticospinal tract, rubrospinal tract, nigrostriatal system, ponto-/olivo-cerebellar fibers, and in the anterolateral system. In contrast, in mice, the reticulospinal tract and ponto-/olivo-cerebellar fibers were the major pathways showing strong DAO activity. In the human corticospinal tract, activity-based staining of DAO did not merge with a motoneuronal marker, but colocalized mostly with excitatory amino acid transporter 2 and in part with GFAP, suggesting that DAO activity-positive cells are astrocytes seen mainly in the motor pathway. These findings establish the distribution of DAO activity in cerebral white matter and the motor system in humans, providing evidence to support the involvement of DAO in schizophrenia and ALS. Our results raise further questions about the regulation of D-serine in DAO-rich regions as well as the physiological/pathological roles of DAO in white matter astrocytes.
Scapular dyskinesis: the surgeon's perspective.

The scapula fulfils many roles to facilitate optimal function of the shoulder. Normal function of the shoulder joint requires a scapula that can be properly aligned in multiple planes of motion of the upper extremity. Scapular dyskinesis, meaning abnormal motion of the scapula during shoulder movement, is a clinical finding commonly encountered by shoulder surgeons. It is best considered an impairment of optimal shoulder function. As such, it may be the underlying cause or the accompanying result of many forms of shoulder pain and dysfunction. The present review looks at the causes and treatment options for this indicator of shoulder pathology and aims to provide an overview of the management of disorders of the scapula.
Drum transcription from polyphonic music with recurrent neural networks

Automatic drum transcription methods aim at extracting a symbolic representation of notes played by a drum kit in audio recordings. For automatic music analysis, this task is of particular interest as such a transcript can be used to extract high level information about the piece, e.g., tempo, downbeat positions, meter, and genre cues. In this work, an approach to transcribe drums from polyphonic audio signals based on a recurrent neural network is presented. Deep learning techniques like dropout and data augmentation are applied to improve the generalization capabilities of the system. The method is evaluated using established reference datasets consisting of solo drum tracks as well as drums mixed with accompaniment. The results are compared to state-of-the-art approaches on the same datasets. The evaluation reveals that F-measure values higher than state of the art can be achieved using the proposed method.