nils-herrmann/clean_scirepeval huggingface dataset:
Heroin epidemics, treatment and ODE modelling.

The UN [United Nations Office on Drugs and Crime (UNODC): World Drug Report, 2005,  Analysis. UNODC, 2005.], EU [European Monitoring Centre for Drugs and Drug Addiction (EMCDDA): Annual Report, 2005.http://annualreport.emcdda.eu.int/en/home-en.html.] and WHO [World Health Organisation (WHO): Biregional Strategy for Harm Reduction, 2005-2009. HIV and Injecting Drug Use.  2005.] have consistently highlighted in recent years the ongoing and persistent nature of opiate and particularly heroin use on a global scale. While this is a global phenomenon, authors have emphasised the significant impact such an epidemic has on individual lives and on society. National prevalence studies have indicated the scale of the problem, but the drug-using career, typically consisting of initiation, habitual use, a treatment-relapse cycle and eventual recovery, is not well understood. This paper presents one of the first ODE models of opiate addiction, based on the principles of mathematical epidemiology. The aim of this model is to identify parameters of interest for further study, with a view to informing and assisting policy-makers in targeting prevention and treatment resources for maximum effectiveness. An epidemic threshold value, R(0), is proposed for the drug-using career. Sensitivity analysis is performed on R(0) and it is then used to examine the stability of the system. A condition under which a backward bifurcation may exist is found, as are conditions that permit the existence of one or more endemic equilibria. A key result arising from this model is that prevention is indeed better than cure.
Wireless sensor networks: a survey on recent developments and potential synergies

Wireless sensor network (WSN) has emerged as one of the most promising technologies for the future. This has been enabled by advances in technology and availability of small, inexpensive, and smart sensors resulting in cost effective and easily deployable WSNs. However, researchers must address a variety of challenges to facilitate the widespread deployment of WSN technology in real-world domains. In this survey, we give an overview of wireless sensor networks and their application domains including the challenges that should be addressed in order to push the technology further. Then we review the recent technologies and testbeds for WSNs. Finally, we identify several open research issues that need to be investigated in future. Our survey is different from existing surveys in that we focus on recent developments in wireless sensor network technologies. We review the leading research projects, standards and technologies, and platforms. Moreover, we highlight a recent phenomenon in WSN research that is to explore synergy between sensor networks and other technologies and explain how this can help sensor networks achieve their full potential. This paper intends to help new researchers entering the domain of WSNs by providing a comprehensive survey on recent developments.
A flexible unmanned aerial vehicle for precision agriculture

An unmanned aerial vehicle (“VIPtero”) was assembled and tested with the aim of developing a flexible and powerful tool for site-specific vineyard management. The system comprised a six-rotor aerial platform capable of flying autonomously to a predetermined point in space, and of a pitch and roll compensated multi-spectral camera for vegetation canopy reflectance recording. Before the flight campaign, the camera accuracy was evaluated against high resolution ground-based measurements, made with a field spectrometer. Then, “VIPtero” performed the flight in an experimental vineyard in Central Italy, acquiring 63 multi-spectral images during 10 min of flight completed almost autonomously. Images were analysed and classified vigour maps were produced based on normalized difference vegetation index. The resulting vigour maps showed clearly crop heterogeneity conditions, in good agreement with ground-based observations. The system provided very promising results that encourage its development as a tool for precision agriculture application in small crops.
Attentive Fashion Grammar Network for Fashion Landmark Detection and Clothing Category Classification

This paper proposes a knowledge-guided fashion network to solve the problem of visual fashion analysis, e.g., fashion landmark localization and clothing category classification. The suggested fashion model is leveraged with high-level human knowledge in this domain. We propose two important fashion grammars: (i) dependency grammar capturing kinematics-like relation, and (ii) symmetry grammar accounting for the bilateral symmetry of clothes. We introduce Bidirectional Convolutional Recurrent Neural Networks (BCRNNs) for efficiently approaching message passing over grammar topologies, and producing regularized landmark layouts. For enhancing clothing category classification, our fashion network is encoded with two novel attention mechanisms, i.e., landmark-aware attention and category-driven attention. The former enforces our network to focus on the functional parts of clothes, and learns domain-knowledge centered representations, leading to a supervised attention mechanism. The latter is goal-driven, which directly enhances task-related features and can be learned in an implicit, top-down manner. Experimental results on large-scale fashion datasets demonstrate the superior performance of our fashion grammar network.
Phishing Website Detection based on Supervised Machine Learning with Wrapper Features Selection

The problem of Web phishing attacks has grown considerably in recent years and phishing is considered as one of the most dangerous Web crimes, which may cause tremendous and negative effects on online business. In a Web phishing attack, the phisher creates a forged or phishing website to deceive Web users in order to obtain their sensitive financial and personal information. Several conventional techniques for detecting phishing website have been suggested to cope with this problem. However, detecting phishing websites is a challenging task, as most of these techniques are not able to make an accurate decision dynamically as to whether the new website is phishing or legitimate. This paper presents a methodology for phishing website detection based on machine learning classifiers with a wrapper features selection method. In this paper, some common supervised machine learning techniques are applied with effective and significant features selected using the wrapper features selection approach to accurately detect phishing websites. The experimental results demonstrated that the performance of the machine learning classifiers was improved by using the wrapper-based features selection. Moreover, the machine learning classifiers with the wrapper-based features selection outperformed the machine learning classifiers with other features selection methods. Phishing website; machine learning; wrapper features selection
Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic Oracles

Parsing accuracy using efficient greedy transition systems has improved dramatically in recent years thanks to neural networks. Despite striking results in dependency parsing, however, neural models have not surpassed stateof-the-art approaches in constituency parsing. To remedy this, we introduce a new shiftreduce system whose stack contains merely sentence spans, represented by a bare minimum of LSTM features. We also design the first provably optimal dynamic oracle for constituency parsing, which runs in amortized O(1) time, compared to O(n) oracles for standard dependency parsing. Training with this oracle, we achieve the best F1 scores on both English and French of any parser that does not use reranking or external data.
Prevalence and Predictors of Maternal Anemia during Pregnancy in Gondar, Northwest Ethiopia: An Institutional Based Cross-Sectional Study

Anaemia is a global public health problem which has an eminence impact on pregnant mother. The aim of this study was to assess the prevalence and predictors of maternal anemia.  A cross-sectional study was conducted from March 1 to April 30, 2012, on 302 pregnant women who attended antenatal care at Gondar University Hospital. Interview-based questionnaire, clinical history, and laboratory tests were used to obtain data. Bivariate and multivariate logistic regression was used to identify predictors.  The prevalence of anemia was 16.6%. Majority were mild type (64%) and morphologically normocytic normochromic (76%) anemia. Anemia was high at third trimester (18.9%). Low family income (AOR [95% CI] = 3.1 [1.19, 8.33]), large family size (AOR [95% CI] = 4.14 [4.13, 10.52]), hookworm infection (AOR [95% CI] = 2.72 [1.04, 7.25]), and HIV infection (AOR [95% CI] = 5.75 [2.40, 13.69]) were independent predictors of anemia.  The prevalence of anemia was high; mild type and normocytic normochromic anemia was dominant. Low income, large family size, hookworm infection, and HIV infection were associated with anemia. Hence, efforts should be made for early diagnosis and management of HIV and hookworm infection with special emphasis on those having low income and large family size.
HAREM: An Advanced NER Evaluation Contest for Portuguese

• HAREM is the first evaluation contest for Named Entity Recognition (NER) in Portuguese. • HAREM features several innovative traits and provides a highly configurable open source evaluator. • There were 10 participants from 6 different countries who submitted 15 official and 6 non-official submissions.  • Identification • Semantic Classification • Morphological Classification • Scenarios • Total • Selective • Types • Absolute • Relative Golden Collection
Fractional-Slot Concentrated-Windings Synchronous Permanent Magnet Machines: Opportunities and Challenges

Fractional-slot concentrated-winding (FSCW) synchronous permanent magnet (PM) machines have been gaining interest over the last few years. This is mainly due to the several advantages that this type of windings provides. These include high-power density, high efficiency, short end turns, high slot fill factor particularly when coupled with segmented stator structures, low cogging torque, flux-weakening capability, and fault tolerance. This paper is going to provide a thorough analysis of FSCW synchronous PM machines in terms of opportunities and challenges. This paper will cover the theory and design of FSCW synchronous PM machines, achieving high-power density, flux-weakening capability, comparison of single- versus double-layer windings, fault-tolerance rotor losses, parasitic effects, comparison of interior versus surface PM machines, and various types of machines. This paper will also provide a summary of the commercial applications that involve FSCW synchronous PM machines.
Cluster Based Routing Protocols: A Comparative Study

Wireless Sensor Network is an infrastructure less network consisting of small, lightweight, low power, and inexpensive wireless sensor nodes which gather and propagate the environmental data. Different routing protocols have been proposed for wireless sensor networks for efficient routing. Each protocol has its own way of routing which vary from each other in terms of the parameters selected or the approach. Cluster based routing is one of the famous routing approaches in which the head node gathers data from all other nodes in the cluster, performs certain aggregation function and then sends that aggregated data to the base station. In this paper i will present the comparative study of few cluster based routing protocols.
Automatically extracting cancer disease characteristics from pathology reports into a Disease Knowledge Representation Model

We introduce an extensible and modifiable knowledge representation model to represent cancer disease characteristics in a comparable and consistent fashion. We describe a system, MedTAS/P which automatically instantiates the knowledge representation model from free-text pathology reports. MedTAS/P is based on an open-source framework and its components use natural language processing principles, machine learning and rules to discover and populate elements of the model. To validate the model and measure the accuracy of MedTAS/P, we developed a gold-standard corpus of manually annotated colon cancer pathology reports. MedTAS/P achieves F1-scores of 0.97-1.0 for instantiating classes in the knowledge representation model such as histologies or anatomical sites, and F1-scores of 0.82-0.93 for primary tumors or lymph nodes, which require the extractions of relations. An F1-score of 0.65 is reported for metastatic tumors, a lower score predominantly due to a very small number of instances in the training and test sets.
Towards improved genome-scale metabolic network reconstructions: unification, transcript specificity and beyond

Genome-scale metabolic network reconstructions provide a basis for the investigation of the metabolic properties of an organism. There are reconstructions available for multiple organisms, from prokaryotes to higher organisms and methods for the analysis of a reconstruction. One example is the use of flux balance analysis to improve the yields of a target chemical, which has been applied successfully. However, comparison of results between existing reconstructions and models presents a challenge because of the heterogeneity of the available reconstructions, for example, of standards for presenting gene-protein-reaction associations, nomenclature of metabolites and reactions or selection of protonation states. The lack of comparability for gene identifiers or model-specific reactions without annotated evidence often leads to the creation of a new model from scratch, as data cannot be properly matched otherwise. In this contribution, we propose to improve the predictive power of metabolic models by switching from gene-protein-reaction associations to transcript-isoform-reaction associations, thus taking advantage of the improvement of precision in gene expression measurements. To achieve this precision, we discuss available databases that can be used to retrieve this type of information and point at issues that can arise from their neglect. Further, we stress issues that arise from non-standardized building pipelines, like inconsistencies in protonation states. In addition, problems arising from the use of non-specific cofactors, e.g. artificial futile cycles, are discussed, and finally efforts of the metabolic modelling community to unify model reconstructions are highlighted.
Fine finger motor skill training with exoskeleton robotic hand in chronic stroke: Stroke rehabilitation

Stroke survivors often show a limited recovery in the hand function to perform delicate motions, such as full hand grasping, finger pinching and individual finger movement. The purpose of this study is to describe the implementation of an exoskeleton robotic hand together with fine finger motor skill training on 2 chronic stroke patients.  Two post-stroke patients participated in a 20-session training program by integrating 10 minutes physical therapy, 20 minutes robotic hand training and 15 minutes functional training tasks with delicate objects(card, pen and coin). These two patients (A and B) had cerebrovascular accident at 6 months and 11 months respectively when enrolled in this study.  The results showed that both patients had improvements in Fugl-Meyer assessment (FM), Action Research Arm Test (ARAT). Patients had better isolation of the individual finger flexion and extension based on the reduced muscle co-contraction from the electromyographic(EMG) signals and finger extension force after 20 sessions of training.  This preliminary study showed that by focusing on the fine finger motor skills together with the exoskeleton robotic hand, it could improve the motor recovery of the upper extremity in the fingers and hand function, which were showed in the ARAT. Future randomized controlled trials are needed to evaluate the clinical effectiveness.
A Novel DC-Side Zero-Voltage Switching (ZVS) Three-Phase Boost PWM Rectifier Controlled by an Improved SVM Method

A novel active clamping zero-voltage switching three-phase boost pulsewidth modulation (PWM) rectifier is analyzed and a modified minimum-loss space vector modulation (SVM) strategy suitable for the novel zero-voltage switching (ZVS) rectifier is proposed in this paper. The topology of the novel ZVS rectifier only adds one auxiliary active switch, one resonant inductor, and one clamping capacitor to the traditional hard-switched three-phase boost PWM rectifier. With the proposed SVM strategy, the novel ZVS rectifier can achieve ZVS for all the main and auxiliary switches. In addition, the antiparallel diodes can be turned OFF softly, so the reverse recovery current is eliminated. Besides, the voltage stress of all the switches is equal to the dc-link voltage. The operation principle and soft-switching condition of the novel ZVS rectifier are analyzed. The design guidelines of the soft switched circuit parameters are described in detail. A DSP controlled 30 kW prototype is implemented to verify the theory.
KEEPING YOUR HEADCOUNT WHEN ALL ABOUT YOU ARE LOSING THEIRS : DOWNSIZING , VOLUNTARY TURNOVER RATES , AND THE MODERATING ROLE OF HR PRACTICES

Although both downsizing and voluntary turnover have been topics of great interest in the organizational literature, little research has addressed the possible relationship between the two. Using organization-level data from multiple industries, we first investigate whether downsizing predicts voluntary turnover rates. Second, to lend support to our causal model, we examine whether this relationship is mediated by aggregated levels of organizational commitment. Third, we test whether the downsizing-turnover rate relationship: (1) is mitigated by HR practices that tend to embed employees in the organization or convey procedural fairness; and (2) is strengthened by HR practices that enhance career development. Results support the hypothesized main, mediated, and moderated effects.
Humanlike shoulder complex for musculoskeletal robot arms

In recent years, musculoskeletal robots are being intensively studied to exploit the advantages of biological musculoskeletal systems for robot developments. In these robots, it is very important to assure engineering, biomechanical, and anatomical plausibility at the same time. However, these requirements are often in contradiction. Especially, in the human's shoulder complex, mimicking the glenohumeral joint and the scapulothoracic joint has been difficult because of the need to assure a wide range of movement and the joint's stability at the same time. In this paper, we propose mechanical structures to realize the functions of the glenohumeral joints and scapulothoracic joint. These structures were used to develop a musculoskeletal robot arm driven by pneumatic artificial muscles. In addition, in order to verify the feasibility of the robot arm, we present a dynamic motion in which the robot arm throws a ball by a simple control strategy.
Hedonism , Achievement , and Power : Universal values that characterize the Dark Triad

Using a sample of Swedes and Americans (N = 385), we attempted to understand the Dark Triad traits (i.e., Machiavellianism, narcissism, and psychopathy) in terms of universal social values. The Dark Triad traits correlated significantly with all 10 value types, forming a sinusoid pattern corresponding to the value model circumplex. In regression analyses, Machiavellianism and narcissism were positively associated with the values Achievement and Power, while psychopathy was positively associated with the values Hedonism, and Power. In addition, the Dark Triad traits explained significant variance over the Big Five traits in accounting for individual differences in social values. Differences between the Swedish and the US sample in the social value Achievement was mediated by the Dark Triad traits, as well as age. Given the unique complex of values accounted for by the Dark Triad traits compared to the Big Five traits, we argue that the former account for a system of self-enhancing ‘‘dark values’’, often hidden but constantly contributing in evaluations of others.
Deciphering Severely Degraded License Plates

Extremely low-quality images, on the order of 20 pixels in width, appear with frustrating frequency in many forensic investigations. Even advanced de-noising and super-resolution technologies are unable to extract useful information from such lowquality images. We show, however, that useful information is present in such highly degraded images. We also show that convolutional neural networks can be trained to decipher the contents of highly degraded images of license plates, and that these networks significantly outperform human observers.  Recognizing text in images is a well-studied problem . Text recognition can either be done by recognizing individual characters or by recognizing the full word. For degraded images, however, it is difficult to localize and recognize individual characters in an image. Word recognition, therefore, has become central to text recognition in degraded images. Deep convolutional neural networks [2] have been used for text recognition in natural images. Goodfellow et al.  used a deep neural network to localize, segment and recognize multiple digits on street view images. Jaderberg et al.  also proposed an end to end text recognition system for natural images using a deep neural network. Jaderberg et al. used a large word dictionary and formulated the text recognition task as a large scale classification problem. Recently, Svoboda et al.  used CNN to remove motion blur from images of license plate blurred with a blur kernel of various directions and lengths. Although this approach is able to deblur highly blurred images, it does not contend with extremely low resolution and noisy images. Unlike much of this previous work we focus on extracting text from highly degraded images on the scale of only a few pixels per character. Hsieh et al.  were the first to show that information can be extracted from highly degraded license plates. In this work the authors assume a known font type, font size, and character layout, and assume that the degraded image is blurry and perspectively distorted, but does not necessarily contain additive noise. Although the authors only show results on a small set of images, they do show that information is present in license plates as small as 20 pixels in width. Building on these ideas, in this paper we propose to train a CNN for recognizing highly degraded license plates with an unknown background template, font type, size and character location. We also explicitly work in the presence of high amounts of additive noise – a common occurrence in real-world imagery. Recognition by Human Observers We begin by performing a perceptual study to determine how well human observers can decipher degraded license plates. This study provides a baseline against which to compare our computational approaches. Observers were shown images of synthetically Figure 1. An example of the type of degraded license plate that we seek to