nils-herrmann/clean_scirepeval huggingface dataset:
Atomoxetine, a selective norepinephrine reuptake inhibitor, improves short term histological outcomes after hypoxic ischemic brain injury in the neonatal male rat
Venue: International Journal of Developmental Neuroscience
Year: 2018

Despite the recent progress of perinatal medicine, perinatal hypoxic ischemic (HI) insult remains an important cause of brain injury in neonates, and is pathologically characterized by neuronal loss and the presence of microglia. Neurotransmitters, such as norepinephrine (NE) and glutamate, are involved in the pathogenesis of hypoxic ischemic encephalopathy via the interaction between neurons and microglia. Although it is well known that the monoamine neurotransmitter NE acts as an anti inflammatory agent in the brain under pathological conditions, its effects on perinatal HI insult remains elusive. Atomoxetine, a selective NE reuptake inhibitor, has been used clinically for the treatment of attention deficit hyperactivity disorder in children. Here, we investigated whether the enhancement of endogenous NE by administration of atomoxetine could protect neonates against HI insult by using the neonatal male rat model. We also examined the involvement of microglia in this process.
Pengaruh Pembelajaran Berbatuan Geogebra terhadap Pemahaman Konsep Matematis ditinjau dari Gaya Kognitif
Year: 2016

The purpose of this research is to know whether there is influence of learning model of Geogebra assisted Discovery Learning to understanding mathematical concept, to know whether there is influence between learners who have cognitive style of FI and FD to understanding mathematical concept, to know whether there is interaction between learning of Discovery Learning Unity Geogebra with the cognitive style of learners towards the understanding of mathematical concepts. This research type is quasi experiment research, population in this research is all students of class VII of SMP Negeri 2 Bandar Lampung, sampling technique in this research use class random technique where class VII 8 as experiment class 1 with study of Discovery Learning assisted geogebra, Class VII 7 as a control class with Discovery Learning learning with Microsoft Power Point. Technique of data analysis in this research use two way Anova test with unequal cell. Based on the test results, the first hypothesis is indicated by the statistical price of 8,871 (p> 4,001) so that H0A is rejected with the conclusion that there is influence of Discovery Learning learning by geogebra toward the understanding of mathematical concept. For the second second hypothesis is shown with a statistical price of 5.23 (p> 4,001) so H0B is rejected with the conclusion that there is an influence between learners who have cognitive style of FI and FD on the understanding of mathematical concepts. For the third hypothesis, it is shown by the statistical price of 1.05 (p <4,001) so H0AB is accepted with no conclusion there is no interaction between the learning of Geogebra assisted Discovery Learning with the cognitive style of learners towards the understanding of mathematical concepts.
BugsJS: a Benchmark of JavaScript Bugs
Venue: 2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST)
Year: 2019

JavaScript is a popular programming language that is also error prone due to its asynchronous, dynamic, and loosely typed nature. In recent years, numerous techniques have been proposed for analyzing and testing JavaScript applications. However, our survey of the literature in this area revealed that the proposed techniques are often evaluated on different datasets of programs and bugs. The lack of a commonly used benchmark limits the ability to perform fair and unbiased comparisons for assessing the efficacy of new techniques. To fill this gap, we propose BugsJS, a benchmark of 453 real, manually validated JavaScript bugs from 10 popular JavaScript server side programs, comprising 444k LOC in total. Each bug is accompanied by its bug report, the test cases that detect it, as well as the patch that fixes it. BugsJS features a rich interface for accessing the faulty and fixed versions of the programs and executing the corresponding test cases, which facilitates conducting highly reproducible empirical studies and comparisons of JavaScript analysis and testing tools.
COMPARISON OF DIFFERENT WAVELET BASES IN THE CASE OF WAVELETS EXPANSIONS OF RANDOM PROCESSES
Year: 2014

In the paper wavelets expansions of random processes are studied. The matter is that although it is enough information for wavelets expansions of deterministic functions, for random processes such theory is weak and it should be developed. The paper investigates uniform convergence of wavelet expansions of Gaussian random processes. The convergence is obtained under simple general conditions on processes and wavelets which can be easily verified. Applications of the developed technique are shown for several wavelet bases. So, conditions of uniform convergence for Battle Lemarie wavelets and Meyer wavelets expansions of Gaussian random processes are presented. Another useful in various computational applications thing is the rate of convergence, especially if we are interested in the optimality of the stochastic approximation or the simulations. An explicit estimate of the rate of uniform convergence for Battle Lemarie wavelets and Meyer wavelets expansions of Gaussian random processes is obtained and compared.
Fixed time cluster synchronization in directed community networks with diverse coupling and different order nodes
Venue: International Journal of Modern Physics B
Year: 2019

In this paper, fixed time cluster synchronization of directed community networks (DCNs) with diverse coupling and different order nodes is explored. For the DCN model, the inherent dynamics and state dimensions of the nodes in different communities can be nonidentical, and moreover, the interactive nodes can differ in their types of connections in different communities and the connections between any pair of different communities can be nonidentical as well. Two types of control strategies, state feedback control and adaptive feedback control, are adopted to force such DCNs to realize cluster synchronization in fixed time. By utilizing reduction to absurdity, some sufficient conditions ensuring the fixed time cluster synchronization are derived for the proposed two control schemes. Moreover, it is revealed theoretically that our estimated settling time is more precise and less conservative than that presented in the previous researches. Finally, the validity of the obtained fixed time cluster synchronization criteria is verified via numerical simulations.
Is dry needling effective for low back pain?
Venue: Medicine
Year: 2018

To evaluate the efficacy and safety of dry needling (DN) for treating low back pain (LBP)  Nine databases were searched from inception to October 2017. Eligible randomized controlled trials (RCTs) involving DN for treating LBP were retrieved. Two reviewers independently screened the articles, extracted data, and evaluated the risk of bias among the included studies using the risk of bias assessment tool by Cochrane Collaboration.  Sixteen RCTs were included and the risk of bias assessment of them was "high" or "unclear" for most domains. Meta analysis results suggested that DN was more effective than acupuncture in alleviating pain intensity and functional disability at postintervention, while its efficacy on pain and disability at follow up was only equal to acupuncture. Besides, DN was superior to sham needling for alleviating pain intensity at postintervention/follow up and functional disability at postintervention. Additionally, qualitative review revealed that DN combined with acupuncture had more significant effect on alleviating pain intensity at postintervention and achieved higher response rate than DN alone. However, compared with other treatments (laser, physical therapy, other combined treatments, etc. it remained uncertain whether the efficacy of DN was superior or equal because the results of included studies were mixed.  Compared with acupuncture and sham needling, DN is more effective for alleviating pain and disability at postintervention in LBP, while its effectiveness on pain and disability at follow up was equal to acupuncture. Besides, it remains uncertain whether the efficacy of DN is superior to other treatments. Nevertheless, considering the overall "high" or "unclear" risk of bias of studies, all current evidence is not robust to draw a firm conclusion regarding the efficacy and safety of DN for LBP. Future RCTs with rigorous methodologies are required to confirm our findings. Details of ethics approval: No ethical approval was required for this systematic review and meta analysis.
A Web Based Interactive Tool for Creating, Inspecting, Editing, and Publishing Etymological Datasets
Venue: EACL
Year: 2017

The paper presents the Etymological DICtionary ediTOR (EDICTOR) a free, interactive, web based tool designed to aid historical linguists in creating, editing, analysing, and publishing etymological datasets. The EDICTOR offers interactive solutions for important tasks in historical linguistics, including facilitated input and segmentation of phonetic transcriptions, quantitative and qualitative analyses of phonetic and morphological data, enhanced interfaces for cognate class assignment and multiple word alignment, and automated evaluation of regular sound correspondences. As a web based tool written in JavaScript, the EDICTOR can be used in standard web browsers across all major platforms.
Geometric Estimation via Robust Subspace Recovery
Venue: ECCV
Year: 2020

Geometric estimation from image point correspondences is the core procedure of many 3D vision problems, which is prevalently accomplished by random sampling techniques. In this paper, we consider the problem from an optimization perspective, to exploit the intrinsic linear structure of point correspondences to assist estimation. We generalize the conventional method to a robust one and extend the previous analysis for linear structure to develop several new algorithms. The proposed solutions essentially address the estimation problem by solving a subspace recovery problem to identify the inliers. Experiments on real world image datasets for both fundamental matrix and homography estimation demonstrate the superiority of our method over the state of the art in terms of both robustness and accuracy.
Ambient Vibration Measurement Data of a Four Story Mass Timber Building
Venue: Front. Built Environ.
Year: 2019

Ambient vibration from wind, traffic, and human activities results into low levels of building motion. As a result, ambient vibration collected data have been used to extract dynamic characteristics of a wide array of civil engineering structures. Among these, a few dynamic characterization tests have been performed on timber building structures, but only a few focused on mass timber structures using different types of engineered wood products for the gravity and/or lateral load resisting systems (Worth et al. 2012; Reynolds et al. 2015, 2016) Since there is a growing interest in design and construction of mass timber structures for multi story buildings, it is important to provide dynamic data obtained from actual constructed projects. The dynamic features of buildings have lead researchers and design professionals to gain knowledge of how structures behave under ambient excitations offering guidance on serviceability performance and insights on the structural performance of structures under high winds and seismic loading. Serviceability performance are of particular relevance for mass timber buildings when compared to conventional concrete and steel buildings, due to the lack of available data. Dynamic data for this type of building is necessary to understand the influence of overall mass, stiffness, and mass distribution on the modal parameters of the structure (frequencies, damping, and mode shapes) especially. The availability of these data can contribute to the effective design of mass timber buildings to ambient and wind induced vibrations and the consequent occupant comfort and safety. In the past decade, cross laminated timber (CLT) has been adopted for structural assemblies of some North American multi story buildings. One of these projects is the "Albina Yard," a fourstory building located in Portland, OR, which is the first multi story construction built using U.S. manufactured cross laminated timber (CLT) floor systems. There is currently a lack of open access test data on existing buildings using mass timber technology. This study addresses this research need. Ambient vibration data were collected on the "Albina Yard," providing one of the few dynamic dataset for a U.S. building constructed using CLT members. An application and discussion of these data using two operational modal analysis (OMA) methods is presented in Mugabo et al. (2019)1. In the cited study, dynamic characteristics of the Albina Yard building were obtained, using the Enhanced Frequency Domain Decomposition (EFDD; Brincker et al. 2001) and the Stochastic Subspace Identification (SSI; Brincker and Andersen, 2006) methods. However, other OMA approaches could be used and results compared to those presented in Mugabo et al. (2019)1. In addition, these collected data can be used for benchmarking future finite element modeling to estimate building performance under extreme loading. For this reason, the complete dynamic dataset for the Albina Yard building is provided to the wider research community for further investigation and dynamic data analyses.
Advanced Extremely Efficient Detection of Replica Nodes in Mobile Wireless Sensor Networks
Venue: JOIV International Journal on Informatics Visualization
Year: 2019

Today, wireless sensor networks (WSNs) are widely used in many applications including the environment, military, and explorations. One of the most dangerous attacks against these networks is node replication. In this attack, the adversary captures a legal node of the network, generates several copies of the node (called, replica nodes) and injects them in the network. Various algorithms have been proposed to handle replica nodes in stationary and mobile WSNs. One of the most well known algorithms to handle this attack in mobile WSNs is eXtremely Efficient Detection (XED) The main idea of XED is to generate and exchange random numbers among neighboring nodes. The XED has some drawbacks including high communication and memory overheads and low speed in the detection of replica nodes. In this paper, an algorithm is presented to improve XED. The proposed algorithm is called Advanced XED (AXED) in which each node observes a few numbers of nodes and whenever two nodes meet, a new random number is generated and exchanged. The efficiency of the proposed algorithm is evaluated in terms of the memory and communication overheads and its results are compared with existing algorithms. The comparison results show that the proposed algorithm imposes lower overheads to the nodes. In addition, the proposed algorithm is simulated and the simulation results show that the proposed algorithm is able to detect replica nodes faster than XED.
Personal Health Knowledge Graphs for Patients
Venue: ArXiv
Year: 2020

Existing patient data analytics platforms fail to incorporate information that has context, is personal, and topical to patients. For a recommendation system to give a suitable response to a query or to derive meaningful insights from patient data, it should consider personal information about the patient's health history, including but not limited to their preferences, locations, and life choices that are currently applicable to them. In this review paper, we critique existing literature in this space and also discuss the various research challenges that come with designing, building, and operationalizing a personal health knowledge graph (PHKG) for patients.
Content based hyperspectral image retrieval using spectral unmixing
Venue: Remote Sensing
Year: 2011

The purpose of content based image retrieval (CBIR) is to retrieve, from real data stored in a database, information that is relevant to a query. A major challenge for the development of efficient CBIR systems in the context of hyperspectral remote sensing applications is how to deal with the extremely large volumes of data produced by current Earth observing (EO) imaging spectrometers. The data resulting from EO campaigns often comprises many Gigabytes per flight. When multiple instruments or timelines are combined, this leads to the collection of massive amounts of data coming from heterogeneous sources, and these data sets need to be effectively stored, managed, shared and retrieved. Furthermore, the growth in size and number of hyperspectral data archives demands more sophisticated search capabilities to allow users to locate and reuse data acquired in the past. In this paper we develop a new strategy to effectively retrieve hyperspectral image data sets using spectral unmixing concepts. Spectral unmixing is a very important task for hyperspectral data exploitation since the spectral signatures collected in natural environments are invariably a mixture of the pure signatures of the various materials found within the spatial extent of the ground instantaneous field view of the imaging instrument. In this work, we use the information provided by spectral unmixing (i.e. the spectral endmembers and their corresponding abundances in the scene) as effective meta data to develop a new CBIR system that can assist users in the task of efficiently searching hyperspectral image instances in large data repositories. The proposed approach is validated using a collection of 154 hyperspectral data sets (comprising seven full flightlines) gathered by NASA using the Airborne Visible Infra Red Imaging Spectrometer (AVIRIS) over the World Trade Center (WTC) area in New York City during the last two weeks of September, 2001, only a few days after the terrorist attacks that collapsed the two main towers and other buildings in the WTC complex.
Diabetic Retinopathy Detection using Deep Learning Techniques
Year: 2020

Diabetic Retinopathy is a type of eye condition induced by diabetes, which damages the blood vessels in the retinal region and the area covered with lesions of varying magnitude determines the severity of the disease. It is one of the most leading causes of blindness amongst the employed community. A variety of factors are observed to play a role in a person to get this disease. Stress and prolonged diabetes are two of the most critical factors to top the list. This disease, if not predicted early, can lead to a permanent impairment of vision. If predicted in advance, the rate of impairment can be brought down or averted. However, it is not easy to detect the presence of this disease, given the time consuming and tedious process of diagnosis. Presently, digital color photographs are evaluated manually by trained clinicians to observe the presence of lesions caused due to vascular abnormalities, which is the major effect of Diabetic Retinopathy. This method, although it is pretty accurate, proves to be costly. The delay brings out the need to automate the diagnosing, which will, in turn, have a significant positive impact on the health sector. In recent times, the adoption of AI in disease diagnosis has ensured promising and reliable results and this serves as the motivation for this journal. The paper employs Deep learning methodologies for automatic detection of Diabetic Retinopathy, resulting in a maximum accuracy of 80% as compared to traditional Machine learning approaches giving only a maximum accuracy of 48% on the same IRDiR Disease Grading Dataset (413 images with 5 levels of DR Training set; 103 images with 5 levels of DR Test set) The data set contains digital fundus images of different levels of Diabetic Retinopathy in discrete frequency distributions.
Serious Gaming to Support Exercise Therapy for Patients with Chronic Nonspecific Low Back Pain: A Feasibility Study.
Venue: Games for health journal
Year: 2018

To investigate the feasibility of a functional exercise program supported by serious gaming for patients with chronic nonspecific low back pain (LBP)  Ten patients with chronic nonspecific LBP and an underlying motor control impairment were recruited. Subjects performed a partially supervised exercise program (36 sessions, 18 weeks) that included 30 minutes of general conditioning and 90 minutes of individually tailored functional motor control exercises (MCEs) Serious games (SGs) were used to (1) improve thoracolumbar dissociation and (2) to provide postural feedback during functional MCEs. The SGs were also available at home.  Treatment satisfaction and the scores on the credibility/expectancy questionnaire were good and did not change throughout the intervention. Patients remained motivated throughout the rehabilitation program and no serious adverse events were reported. Overall, participants indicated that the SGs helped them to perform the home exercises more correctly, and as a consequence, they felt more confident doing them. However, the time needed to set up the games was a barrier for home use and participants would have found it useful to receive postural feedback during daily life activities.  It is feasible to support a functional exercise program with SGs for patients with chronic nonspecific LBP, both in a supervised and a home environment. Time efficiency and the integration of SGs in daily life activities are challenges that need to be addressed in the future.
Visual predictive control from distance based and Homography based Features
Venue: 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO)
Year: 2016

This paper deals with the issue of improved performance for image based visual servoing combining features selection and constraints handling. Observed object is described as a set of 3D points, corresponding 2D pixel points features in camera view are extracted and transformed to spherical point features through a virtual projection process; then, a set of six independent visual features combining distance based and Homography based Features, is determined and the related feature Jacobian is calculated. After features selection, image based visual servoing (IBVS) is formulated into a constrained optimization problem by nonlinear model predictive control (NMPC) method. Visibility constraints, 3D velocities constraints and 3D task space constraints are all considered in the optimization problem. Finally, simulations are provided and demonstrate the effectiveness and improved behavior of proposed method.