nils-herrmann/clean_scirepeval huggingface dataset:
Exploiting pivot words to classify and summarize discourse facets of scientific papers
Venue: Scientometrics
Year: 2020

The ever increasing number of published scientific articles has prompted the need for automated, data driven approaches to summarizing the content of scientific articles. The Computational Linguistics Scientific Document Summarization Shared Task (CL SciSumm 2019) has recently fostered the study and development of new text mining and machine learning solutions to the summarization problem customized to the academic domain. In CL SciSumm, a Reference Paper (RP) is associated with a set of Citing Papers (CPs) all containing citations to the RP. In each CP, the text spans (i.e. citances) have been identified that pertain to a particular citation to the RP. The task of identifying the spans of text in the RP that most accurately reflect the citance is addressed using supervised approaches. This paper proposes a new, more effective solution to the CL SciSumm discourse facet classification task, which entails identifying for each cited text span what facet of the paper it belongs to from a predefined set of facets. It proposes also to extend the set of traditional CL SciSumm tasks with a new one, namely the discourse facet summarization task. The idea behind is to extract facet specific descriptions of each RP consisting of a fixed length collection of RP's text spans. To tackle both the standard and the new tasks, we propose machine learning supported solutions based on the extraction of a selection of discriminating words, called pivot words Predictive features based on pivot words are shown to be of great importance to rate the pertinence and relevance of a text span to a given facet. The newly proposed facet classification method performs significantly better than the best performing CL SciSumm 2019 participant (i.e. the classification accuracy has increased by 8% whereas regression methods achieved promising results for the newly proposed summarization task.
Development and Application of High Sensitivity Wireless Smart Sensors for Decentralized Stochastic Modal Identification
Year: 2012

State of the art smart sensor technology enables deployment of dense arrays of sensors, which is critical for structural health monitoring (SHM) of complicated and large scale civil structures. Despite recent successful implementation of various wireless smart sensor networks (WSSNs) for full scale SHM, the low cost micro electro mechanical systems (MEMS) sensors commonly used in smart sensors cannot readily measure low level ambient vibrations because of their relatively low resolution. Combined use of conventional wired high sensitivity sensors with low cost wireless smart sensors has been shown to provide improved spectral estimates of response that can lead to improved experimental modal analysis. However, such a heterogeneous network of wired and wireless sensors requires central collection of an enormous amount of raw data and off network processing to achieveglobal time synchronization; consequently, many of the advantages of WSSNs for SHM are lost. In this paper, the development of a new high sensitivity accelerometer board (SHM H) for the Imote2 wireless smart sensor (WSS) platform is presented. The use of a small number of these high sensitivity WSSs, composed of the SHM H and Imote2, as reference sensors in the Natural Excitation Technique based decentralized WSSN strategy is explored and is shown to provide a cost effective means of improving modal feature extraction in the decentralized WSSN for SHM.  10.1061/(ASCE)EM.1943 7889 .0000352.  Structural health monitoring; Probe instruments; Identification; Stochastic models.  Structural health monitoring; Wireless smart sensor network; High sensitivity sensor; System identification; Decentralized sensor network.
Machine Comprehension with Modified Bi Directional Attention Flow Network
Year: 2017

The advancement of of Machine Comprehension (MC) in recent years can be largely attributed to the attention mechanism. We proposed a filtering layer in the multi layer hierarchical neural network architecture that can potentially enhance the performance of the attention mechanism. Our preliminary experimentation on the Stanford Question Answering Dataset (SQuAD) showed that the filtering layer can effectively assist the attention flow layer in Bi Directional Attention Flow (BiDAF) model.
Studies on the unfolding of the atmospheric neutrino spectrum with IceCube 59 using the TRUEE algorithm
Year: 2012

The measurement of the atmospheric neutrino energy spectrum provides information about the diffuse neutrino flux from extragalactic sources. A relative increa se of the spectrum toward higher energies could be evidence for neutrino producing hadronic processes in the cosmic high energy accelerators, such as active galactic nuclei or gamma ray bursts. IceCube is currently the largest neutrino detector on Earth and is placed in the antarctic ice at the geographic South Pole. IceCube permits the detection of neutrinos with energies beyond 10 6 GeV. Since the acceptance and the resolution of neutrino telescopes suffer from the fin ite resolution and limited acceptance, a regularized unfol ding method is used to extract the energy distribution of neutrinos from the measured observables. For AMANDA, the unfolding was done with the RUN algorithm. Based on the basic concept of this program and for data analyses in the ROOT frame, a new deconvolution algorithm (TRUEE) has been written and tested. With this new algorithm, stud ies on the analysis of the atmospheric neutrino spectrum measured with the IceCube 59 string configuration are presented
Cable force monitoring and prediction for cable group of long span cable supported bridges
Year: 2018

Cable force monitoring is an essential and critical part of structural health monitoring for long span cable supported bridges. Considering economical and efficient issues, the accuracy and quality of safety assessment depend considerably on a reasonable cable monitoring scheme, especially the number and locations of limited sensors. This paper presents the optimal sensor placement for cable force monitoring and cable force prediction in non sensor positions with optimal sensor arrangement. Bond energy algorithm was used to obtain optimal sensor placement strategy, where mutual information was employed to describe the inherent spatial correlation of cable group. To maximize useful cable force information of cable group utilizing from optimal sensor arrangement, particle swarm optimization based Kernel Extreme Learning Machine model was employed for forecasting cable forces in non sensor positions. The analysis results illustrated that the proposed kernel extreme learning machine can achieve better predictive performance than multiple linear regression model and multiple adaptive spline regression model with higher accuracy and generalization. The cable force monitoring and prediction with notable decrease of sensor number lays a comprehensive basis for bridge safety assessment and verified the effectiveness of the proposed method.
Statistical Approaches to the Inverse Problem
Year: 2011

Magnetoencephalography (MEG) can be considered as one of the most powerful instruments for non invasive investigation of the brain functions (Hamalainen et al. 1993) Its good temporal resolution (sampling frequency can reach several thousands of Hertz) only comparable to the sampling frequency of electroencephalography (EEG) allows following the neural dynamics on amillisecond time scale. This implies that, with respect to other functional imaging modalities, MEG provides an enormous amount of information about the brain activity. On the other hand, localization of brain activity from MEG data requires to solve a highly ill posed inverse problem (Sarvas, 1987) exact reconstruction of the neural current is not possible even with noise free data, and the debate about the actual spatial resolution of MEG is still open. Despite this drawback, the appeal of MEG data is such that a large number of methods have been developed for localizing brain activity from MEG recordings. Starting from the middle Eighties, when the first MinimumNorm Estimate (Hamalainen Ilmoniemi, 1984) appeared, to the recent developments in spatio temporal regularization (Ou et al. 2009) state space models (Long et al. 2006; Sorrentino et al. 2009) and Markov Chain Monte Carlo (Jun et al. 2005) the attempts to insert enough a priori information to actually provide stable and reliable solutions have been rising rather than diminishing. The goal and the scope are clear: automatic and reliable source localization can be of practical use in several fields, possibly including clinical applications (pre surgical evaluation, foci localization in epilepsy) new technology development (e.g. brain computer interfaces) and scientific research (e.g. for analysis of large datasets) Despite having been presented in rather different frameworks and with largely different names and techniques, most methods appeared so far in the MEG inverse literature can be described rather straightforwardly in a statistical setting, which helps unifying and clarifying the assumptions and the limitations of the methods. In this chapter, we attempt a brief review of the most largely known methods, as well as of some of the most recently appeared ones, highlighting the statistical features. We first provide a brief description of the statistical approach to inverse problems in Section 2; then we describe the main features of the MEG inverse problem in Section 3; in Section 4 and 5 we review methods developed by other authors, while in Section 6 we discuss our work on Bayesian filtering for MEG.
4D GRAIN GROWTH STUDY BY LABORATORY DIFFRACTION CONTRAST TOMOGRAPHY
Year: 2017

Using a novel laboratory diffraction contrast tomography (LabDCT) a non destructive, 4D study was conducted to investigate the 3D grain structural evolution during grain growth. Information of both the morphology and crystallographic orientation for the grains is obtained. The capability of applying LabDCT for detailed study of grain growth is demonstrated, promising to provide the necessary 4D experimental evidences for further understandings of grain growth.
Monitoring and mapping non governmental conservation action in Amazonia
Year: 2020

The vast forests of Amazonia are a major focus of conservation initiatives by NGOs and governments. Nevertheless, the dynamics and geography of conservation action in this iconic region is poorly understood and, unlike scientific production, has never been systematically assessed. Here, we address this deficit through a survey of ongoing conservation NGO project sites in the Amazon region. We identified a total of 170 conservation NGOs that were implementing 378 projects at 518 sites across the breadth of the Amazon region. Project sites were associated with inhabited regions near major rivers, but away from major urban centres. In contrast to scientific research sites, there was a relatively high representation of conservation NGO project sites in the so called `arc of deforestation` As anticipated, conservation project sites were often situated within protected areas. Overall, our study shows that, as a sector, conservation NGOs have successfully implemented projects throughout the Amazon region and are seemingly well placed to meet future conservation challenges. However, given the practical difficulties we encountered while collecting the data for this project, we strongly recommend the development of an online data platform for the entire Amazon region that would allow conservationists and scientists to share information, monitor governmental and non governmental actions, and better coordinate conservation responses in the future.
Effects of Resistance Training in Youth Athletes on Muscular Fitness and Athletic Performance: A Conceptual Model for Long Term Athlete Development
Venue: Front. Physiol.
Year: 2016

During the stages of long term athlete development (LTAD) resistance training (RT) is an important means for (i) stimulating athletic development, (ii) tolerating the demands of long term training and competition, and (iii) inducing long term health promoting effects that are robust over time and track into adulthood. However, there is a gap in the literature with regards to optimal RT methods during LTAD and how RT is linked to biological age. Thus, the aims of this scoping review were (i) to describe and discuss the effects of RT on muscular fitness and athletic performance in youth athletes, (ii) to introduce a conceptual model on how to appropriately implement different types of RT within LTAD stages, and (iii) to identify research gaps from the existing literature by deducing implications for future research. In general, RT produced small to moderate effects on muscular fitness and athletic performance in youth athletes with muscular strength showing the largest improvement. Free weight, complex, and plyometric training appear to be well suited to improve muscular fitness and athletic performance. In addition, balance training appears to be an important preparatory (facilitating) training program during all stages of LTAD but particularly during the early stages. As youth athletes become more mature, specificity, and intensity of RT methods increase. This scoping review identified research gaps that are summarized in the following and that should be addressed in future studies: (i) to elucidate the influence of gender and biological age on the adaptive potential following RT in youth athletes (especially in females) (ii) to describe RT protocols in more detail (i.e. always report stress and strain based parameters) and (iii) to examine neuromuscular and tendomuscular adaptations following RT in youth athletes.
Gestion du site extractionnel dans les traitements implantaires du secteur anterieur maxillaire
Year: 2015

L'implantologie permet de remplacer des dents absentes ou condamnees a l'extraction, restituant ainsi la fonction et l'esthetique au patient. L'extraction dentaire du secteur antero maxillaire entraine une resorption partielle ou totale de la corticale vestibulaire due principalement a la perte de la vascularisation desmodontale, accompagnee de remaniements tissulaires. Elle doit etre au maximum conservatrice en tissus osseux et muqueux, et fait appel a des moyens et techniques specifiques comme la piezochirurgie. L'evolution des (bio materiaux et des concepts implantaires permet aujourd'hui de raccourcir les delais entre l'extraction et l'implantation. La decision du protocole a suivre est extemporanee a l'extraction et les choix sont les suivants implantation immediate, precoce, differee ou tardive. Dans des conditions tissulaires ideales, l'implantation post extractionnelle est indiquee. Dans des situations cliniques moins favorables, l'attente de quelques jours a quelques semaines apres l'extraction permet de gerer le contexte osteo muqueux et de l'optimiser, par le moyen de greffes osseuses et/ou muqueuses.
A case of Behcet's disease and Graves' thyrotoxicosis co existing in the same patient: a rare combination
Venue: Clinical Medicine
Year: 2019

Behcet's disease is a rare autoimmune vasculitic disorder that is characterised by a triple symptom complex of recurrent oral aphthous ulcers, genital ulcers and uveitis. The disease is named after the Turkish dermatologist Hulusi Behcet, who identified it in a patient in 1924 and published a
Hallucinating IDT Descriptors and I3D Optical Flow Features for Action Recognition With CNNs
Venue: 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2019

In this paper, we revive the use of old fashioned handcrafted video representations for action recognition and put new life into these techniques via a CNN based hallucination step. Despite of the use of RGB and optical flow frames, the I3D model (amongst others) thrives on combining its output with the Improved Dense Trajectory (IDT) and extracted with its low level video descriptors encoded via Bag of Words (BoW) and Fisher Vectors (FV) Such a fusion of CNNs and handcrafted representations is time consuming due to pre processing, descriptor extraction, encoding and tuning parameters. Thus, we propose an end to end trainable network with streams which learn the IDT based BoW/FV representations at the training stage and are simple to integrate with the I3D model. Specifically, each stream takes I3D feature maps ahead of the last 1D conv. layer and learns to `translate' these maps to BoW/FV representations. Thus, our model can hallucinate and use such synthesized BoW/FV representations at the testing stage. We show that even features of the entire I3D optical flow stream can be hallucinated thus simplifying the pipeline. Our model saves 20 55h of computations and yields state of the art results on four publicly available datasets.
Non equispaced B spline wavelets
Venue: Int. J. Wavelets Multiresolution Inf. Process.
Year: 2016

This paper has three main contributions. The first is the construction of wavelet transforms from B spline scaling functions defined on a grid of non equispaced knots. The new construction extends the equispaced, biorthogonal, compactly supported Cohen Daubechies Feauveau wavelets. The new construction is based on the factorisation of wavelet transforms into lifting steps. The second and third contributions are new insights on how to use these and other wavelets in statistical applications. The second contribution is related to the bias of a wavelet representation. It is investigated how the fine scaling coefficients should be derived from the observations. In the context of equispaced data, it is common practice to simply take the observations as fine scale coefficients. It is argued in this paper that this is not acceptable for non interpolating wavelets on non equidistant data. Finally, the third contribution is the study of the variance in a non orthogonal wavelet transform in a new framework, replacing the numerical condition as a measure for non orthogonality. By controlling the variances of the reconstruction from the wavelet coefficients, the new framework allows us to design wavelet transforms on irregular point sets with a focus on their use for smoothing or other applications in statistics.
Photocatalysis: an overview of recent developments and technological advancements
Venue: Science China Chemistry
Year: 2019

Photocatalysis, which is the catalyzation of redox reactions via the use of energy obtained from light sources, is a topic that has garnered a lot of attention in recent years as a means of addressing the environmental and economic issues plaguing society today. Of particular interest are photosynthesis can potentially mimic a variety of vital reactions, many of which hold the key to develop sustainable energy economy. In light of this, many of the technological and procedural advancements that have recently occurred in the field are discussed in this review, namely those linked to: (1) photocatalysts made from metal oxides, nitride, and sulfides; (2) photocatalysis via polymeric carbon nitride (PCN) and (3) general advances and mechanistic insights related to TiO 2 based catalysts. The challenges and opportunities that have arisen over the past few years are discussed in detail. Basic concepts and experimental procedures which could be useful for eventually overcoming the problems associated with photo catalysis are presented herein.
Foreword to the research front on 'Microplastics in Soils'
Year: 2019

Knowledge about the transport of microplastics in soils is necessary to correctly assess their possible transfer to ground and surface waters as well as their impact in the soils themselves. Microplastics mostly enter soils from the surface, but little is known about their later distribution and fate. Yu et al. (2019) explore whether biogenic activities can be a pathway for microplastics to be transported in soils and into groundwater, and conclude that biogenic activities need to be considered in microplastic dynamics in soil systems. The role of microplastics as vectors for organic micropollutants has been widely studied in aquatic systems, but much less so in soils. Rodriguez Seijo et al. (2019) investigate whether earthworms (Eisenia fetida) could be carriers of pesticides in these media. Interestingly, they could not demonstrate thatmicroplastics can be carriers of pesticides to earthworms, but found that these organisms avoided pesticide contaminated microplastics. CSIRO PUBLISHING
Conventional vs Microwave assisted SiO2/P2O5 catalyzed synthesis of Schiff bases
Year: 2020

Here in this work we have carried out synthesis of isoniazid and their benzaldehyde derivative in the presence of SiO2/P2O5 catalyzed utilizing conventional and microwave assisted protocols. Both these protocols examined comparatively and we found that microwave assisted synthesis is more efficient, eco friendly and economical method as compared to conventional method. Yield of product is also higher than conventional method. Use of heterogeneous catalyst is also promoting the yield of product and it is very easy to separate from the product by simple filtration and centrifugation. These synthesized compounds are characterized by physical and spectral studies (IR, UV VIS, NMR spectroscopy) Isoniazid having excellent antitubercular activity and due to presence of Isoniazid they may have good antibiotic property.
Security and privacy issues affecting cloud based IP camera
Venue: 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)
Year: 2017

Consumers have overwhelmingly adopted the use of Internet of Things (IoT) inside their homes, these devices are connected to the internet sending and receiving data 24/7, a security breach or privacy exposure from one of these devices could lead to more nightmares than horror movies. These have lead us to review the current capabilities of Cloud Based IP Cameras, its existing security and privacy issues affecting these devices. We present an analysis of the main privacy and security concerns currently affecting thousands of consumers IP Cameras globally, the uncertainty of not knowing what happens to your data when is stored in the Cloud and propose set of recommendations to help protect consumer's IoT devices in the intimacy of their home. Stop those nightmares before they happen because there is no peace of mind knowing an unknown person may be watching your children baby monitor and tracking all your movement inside your home. Although there are still challenges to address, is better to be an educated consumer who knows and understand the risk of these devices and take measures to prevent unwanted exposure.