nils-herrmann/clean_scirepeval huggingface dataset:
A Framework for Distributed Semantic Annotation of Musical Score: "Take It to the Bridge!"

Music notation expresses performance instructions in a way commonly understood by musicians, but printed paper parts are limited to encodings of static, a priori knowledge. In this paper we present a platform for multi-way communication between collaborating musicians through the dynamic modification of digital parts: the Music Encoding and Linked Data (MELD) framework for distributed real-time annotation of digital music scores. MELD users and software agents create semantic annotations of music concepts and relationships, which are associated with musical structure specified by the Music Encoding Initiative schema (MEI). Annotations are expressed in RDF, allowing alternative music vocabularies (e.g., popular vs. classical music structures) to be applied. The same underlying framework retrieves, distributes, and processes information that addresses semantically distinguishable music elements. Further knowledge is incorporated from external sources through the use of Linked Data. The RDF is also used to match annotation types and contexts to rendering actions which display the annotations upon the digital score. Here, we present a MELD implementation and deployment which augments the digital music scores used by musicians in a group performance, collaboratively changing the sequence within and between pieces in a set list.
Assessment of methodological quality of primary studies by systematic reviews: results of the metaquality cross sectional study.

To describe how the methodological quality of primary studies is assessed in systematic reviews and whether the quality assessment is taken into account in the interpretation of results.



Cochrane systematic reviews and systematic reviews in paper based journals.



965 systematic reviews (809 Cochrane reviews and 156 paper based reviews) published between 1995 and 2002.



The methodological quality of primary studies was assessed in 854 of the 965 systematic reviews (88.5%). This occurred more often in Cochrane reviews than in paper based reviews (93.9% v 60.3%, P < 0.0001). Overall, only 496 (51.4%) used the quality assessment in the analysis and interpretation of the results or in their discussion, with no significant differences between Cochrane reviews and paper based reviews (52% v 49%, P = 0.58). The tools and methods used for quality assessment varied widely.



Cochrane reviews fared better than systematic reviews published in paper based journals in terms of assessment of methodological quality of primary studies, although they both largely failed to take it into account in the interpretation of results. Methods for assessment of methodological quality by systematic reviews are still in their infancy and there is substantial room for improvement.
Smoking and cervical cancer: pooled analysis of the IARC multi-centric case–control study

Smoking has long been suspected to be a risk factor for cervical cancer. However, not all previous studies have properly controlled for the effect of human papillomavirus (HPV) infection, which has now been established as a virtually necessary cause of cervical cancer. To evaluate the role of smoking as a cofactor of progression from HPV infection to cancer, we performed a pooled analysis of 10 previously published case–control studies. This analysis is part of a series of analyses of cofactors of HPV in the aetiology of cervical cancer.  Data were pooled from eight case–control studies of invasive cervical carcinoma (ICC) and two of carcinoma in situ (CIS) from four continents. All studies used a similar protocol and questionnaires and included a PCR-based evaluation of HPV DNA in cytological smears or biopsy specimens. Only subjects positive for HPV DNA were included in the analysis. A total of 1463 squamous cell ICC cases were analyzed, along with 211 CIS cases, 124 adeno- or adeno-squamous ICC cases and 254 control women. Pooled odds ratios (OR) and 95% confidence intervals (CI) were estimated using logistic regression models controlling for sexual and non-sexual confounding factors.  There was an excess risk for ever smoking among HPV positive women (OR 2.17 95%CI 1.46–3.22). When results were analyzed by histological type, an excess risk was observed among cases of squamous cell carcinoma for current smokers (OR 2.30, 95%CI 1.31–4.04) and ex-smokers (OR 1.80, 95%CI 0.95–3.44). No clear pattern of association with risk was detected for adenocarcinomas, although the number of cases with this histologic type was limited.  Smoking increases the risk of cervical cancer among HPV positive women. The results of our study are consistent with the few previously conducted studies of smoking and cervical cancer that have adequately controlled for HPV infection. Recent increasing trends of smoking among young women could have a serious impact on cervical cancer incidence in the coming years.
Analysis of Common-Mode Noise for Weakly Coupled Differential Serpentine Delay Microstrip Line in High-Speed Digital Circuits

This study investigates the mechanisms of generation of the transient transmission common-mode noise in a differential serpentine delay line under weak coupling condition. The generation mechanism and the frequency of common-mode noise are investigated with reference to the time-domain transmission waveform and the differential-to-common mode conversion mixed-mode S-parameters using the circuit solver HSPICE and 3-D full-wave simulator HFSS, respectively. The generation mechanisms of common-mode noise include length mismatch between vertical-turn-coupled traces, the length effect of parallel-coupled traces, and the crosstalk noise effect. Moreover, a graphical method based on wave tracing is presented to illustrate the cancellation mechanism of near-end common-mode noise for the symmetrical differential serpentine delay line. Some practical, commonly used layout routings of the differential serpentine delay line are investigated. Some important design guidelines are provided to help design differential serpentine delay line with low common-mode noise. A comparison between simulated and measured results validates the equivalent circuit model and analytical approach.
Children learning with a social robot

We used a social robot as a teaching assistant in a class for children's collaborative learning. In the class, a group of 6th graders learned together using Lego Mindstorms. The class consisted of seven lessons with Robovie, a social robot, followed by one lesson to test their individual achievement. Robovie managed the class and explained how to use Lego Mindstorms. In addition to such basic management behaviors for the class, we prepared social behaviors for building relationships with the children and encouraging them. The result shows that the social behavior encouraged children to work more in the first two lessons, but did not affect them in later lessons. On the other hand, social behavior contributed to building relationships and attaining better social acceptance.
Automatic identification of heart failure diagnostic criteria, using text analysis of clinical notes from electronic health records

Early detection of Heart Failure (HF) could mitigate the enormous individual and societal burden from this disease. Clinical detection is based, in part, on recognition of the multiple signs and symptoms comprising the Framingham HF diagnostic criteria that are typically documented, but not necessarily synthesized, by primary care physicians well before more specific diagnostic studies are done. We developed a natural language processing (NLP) procedure to identify Framingham HF signs and symptoms among primary care patients, using electronic health record (EHR) clinical notes, as a prelude to pattern analysis and clinical decision support for early detection of HF.



We developed a hybrid NLP pipeline that performs two levels of analysis: (1) At the criteria mention level, a rule-based NLP system is constructed to annotate all affirmative and negative mentions of Framingham criteria. (2) At the encounter level, we construct a system to label encounters according to whether any Framingham criterion is asserted, denied, or unknown.



Precision, recall, and F-score are used as performance metrics for criteria mention extraction and for encounter labeling.



Our criteria mention extractions achieve a precision of 0.925, a recall of 0.896, and an F-score of 0.910. Encounter labeling achieves an F-score of 0.932.



Our system accurately identifies and labels affirmations and denials of Framingham diagnostic criteria in primary care clinical notes and may help in the attempt to improve the early detection of HF. With adaptation and tooling, our development methodology can be repeated in new problem settings.
Author Disambiguation in PubMed: Evidence on the Precision and Recall of Author-ity among NIH-Funded Scientists

We examined the usefulness (precision) and completeness (recall) of the Author-ity author disambiguation for PubMed articles by associating articles with scientists funded by the National Institutes of Health (NIH). In doing so, we exploited established unique identifiers-Principal Investigator (PI) IDs-that the NIH assigns to funded scientists. Analyzing a set of 36,987 NIH scientists who received their first R01 grant between 1985 and 2009, we identified 355,921 articles appearing in PubMed that would allow us to evaluate the precision and recall of the Author-ity disambiguation. We found that Author-ity identified the NIH scientists with 99.51% precision across the articles. It had a corresponding recall of 99.64%. Precision and recall, moreover, appeared stable across common and uncommon last names, across ethnic backgrounds, and across levels of scientist productivity.
Fast image segmentation on mobile phone using multi-level graph cut

This paper presents a system for an efficient image segmentation on mobile phones using multi-level graph cut. As the computational capacity of mobile devices is often limited, a fluent and smooth image segmentation is a challenging task with existing segmentation algorithms, increased in difficulty by mobile phone cameras continually upgraded to take photos of higher resolution. Our solution is to carefully tweak the classic graph cut algorithm for an interactive image cutout, enhancing the performance without compromising the quality of the segmentation. This is achieved by downsampling the original high-resolution image and selecting a rough cutout region on this low-resolution image with a superpixel based pre-segmentation. The segmented foreground is then mapped back to the full-size image and the image undergoes an adaptive boundary refinement. This second segmentation performs the optimization locally and can be accomplished within milliseconds. We test our system on an Apple iPhone 6 and our experiments show that a high quality segmentation can be achieved in a lag-free manner on the mobile phone even for multi-megapixel images.
Programming by choice: urban youth learning programming with scratch

This paper describes Scratch, a visual, block-based programming language designed to facilitate media manipulation for novice programmers. We report on the Scratch programming experiences of urban youth ages 8-18 at a Computer Clubhouse 'an after school center' over an 18-month period. Our analyses of 536 Scratch projects collected during this time documents the learning of key programming concepts even in the absence of instructional interventions or experienced mentors. We discuss the motivations of urban youth who choose to program in Scratch rather than using one of the many other software packages available to them and the implications for introducing programming at after school settings in underserved communities.
Segmentation and Reconstruction of Polyhedral Building Roofs From Aerial Lidar Point Clouds

This paper presents a solution framework for the segmentation and reconstruction of polyhedral building roofs from aerial LIght Detection And Ranging (lidar) point clouds. The eigenanalysis is first carried out for each roof point of a building within its Voronoi neighborhood. Such analysis not only yields the surface normal for each lidar point but also separates the lidar points into planar and nonplanar ones. In the second step, the surface normals of all planar points are clustered with the fuzzy k-means method. To optimize this clustering process, a potential-based approach is used to estimate the number of clusters, while considering both geometry and topology for the cluster similarity. The final step of segmentation separates the parallel and coplanar segments based on their distances and connectivity, respectively. Building reconstruction starts with forming an adjacency matrix that represents the connectivity of the segmented planar segments. A roof interior vertex is determined by intersecting all planar segments that meet at one point, whereas constraints in the form of vertical walls or boundary are applied to determine the vertices on the building outline. Finally, an extended boundary regularization approach is developed based on multiple parallel and perpendicular line pairs to achieve topologically consistent and geometrically correct building models. This paper describes the detail principles and implementation steps for the aforementioned solution framework. Results of a number of buildings with diverse roof complexities are presented and evaluated.
Recognizing Abnormal Heart Sounds Using Deep Learning

The work presented here applies deep learning to the task of automated cardiac auscultation, i.e. recognizing abnormalities in heart sounds. We describe an automated heart sound classification algorithm that combines the use of time-frequency heat map representations with a deep convolutional neural network (CNN). Given the cost-sensitive nature of misclassification, our CNN architecture is trained using a modified loss function that directly optimizes the trade-off between sensitivity and specificity. We evaluated our algorithm at the 2016 PhysioNet Computing in Cardiology challenge where the objective was to accurately classify normal and abnormal heart sounds from single, short, potentially noisy recordings. Our entry to the challenge achieved a final specificity of 0.95, sensitivity of 0.73 and overall score of 0.84. We achieved the greatest specificity score out of all challenge entries and, using just a single CNN, our algorithm differed in overall score by only 0.02 compared to the top place finisher, which used an ensemble approach.
Data processing and semantics for advanced internet of things (IoT) applications: modeling, annotation, integration, and perception

This tutorial presents tools and techniques for effectively utilizing the Internet of Things (IoT) for building advanced applications, including the Physical-Cyber-Social (PCS) systems. The issues and challenges related to IoT, semantic data modelling, annotation, knowledge representation (e.g. modelling for constrained environments, complexity issues and time/location dependency of data), integration, analysis, and reasoning will be discussed. The tutorial will describe recent developments on creating annotation models and semantic description frameworks for IoT data (e.g. such as W3C Semantic Sensor Network ontology). A review of enabling technologies and common scenarios for IoT applications from the data and knowledge engineering point of view will be discussed. Information processing, reasoning, and knowledge extraction, along with existing solutions related to these topics will be presented. The tutorial summarizes state-of-the-art research and developments on PCS systems, IoT related ontology development, linked data, domain knowledge integration and management, querying large-scale IoT data, and AI applications for automated knowledge extraction from real world data.
Passive Self-Interference Suppression for Full-Duplex Infrastructure Nodes

Recent research results have demonstrated the feasibility of full-duplex wireless communication for short-range links. Although the focus of the previous works has been active cancellation of the self-interference signal, a majority of the overall self-interference suppression is often due to passive suppression, i.e., isolation of the transmit and receive antennas. We present a measurement-based study of the capabilities and limitations of three key mechanisms for passive self-interference suppression: directional isolation, absorptive shielding, and cross-polarization. The study demonstrates that more than 70 dB of passive suppression can be achieved in certain environments, but also establishes two results on the limitations of passive suppression: (1) environmental reflections limit the amount of passive suppression that can be achieved, and (2) passive suppression, in general, increases the frequency selectivity of the residual self-interference signal. These results suggest two design implications: (1) deployments of full-duplex infrastructure nodes should minimize near-antenna reflectors, and (2) active cancellation in concatenation with passive suppression should employ higher-order filters or per-subcarrier cancellation.
Generation of Panoramic View from 360 Degree Fisheye Images Based on Angular Fisheye Projection

this paper proposes a algorithm to generate the panoramic view from 360 ¡ãfisheye images based on angular fisheye projection model. Firstly, the image plane will be mapped to the view plane, the sphere plane, to obtain the coordinates of spherical points. Then use the angular fisheye projection model to get fisheye images' radius. Secondly, calculate the coordinates of fisheye images' points with the radius of fisheye images and the coordinates of spherical points. Finally, map the points of fisheye images to the target plane by the way of backward mapping. Experiments show that this algorithm is simple and fast, and can obtain the ideal images.
Local voxelizer: A shape descriptor for surface registration

Surface registration brings multiple scans into a common coordinate system by aligning their overlapping components. This can be achieved by finding a few pairs of matched points on different scans using local shape descriptors and employing the matches to compute transformations to produce the alignment. By defining a unique local reference frame (LRF) and attaching an LRF to shape descriptors, the transformation can be computed using only one match based on aligning the LRFs. This paper proposes a local voxelizer descriptor, and the key ideas are to define a unique LRF using the support around a basis point, to perform voxelization for the local shape within a cubical volume aligned with the LRF, and to concatenate local features extracted from each voxel to construct the descriptor. An automatic rigid registration approach is given based on the local voxelizer and an expanding strategy that merges descriptor representations of aligned scans. Experiments show that our registration approach allows the acquisition of 3D models of various objects, and that the local voxelizer is robust to mesh noise and varying mesh resolution, in comparison to two state-of-the-art shape descriptors.
TDOA Estimation for Multiple Sound Sources in Noisy and Reverberant Environments Using Broadband Independent Component Analysis

In this paper, we show that minimization of the statistical dependence using broadband independent component analysis (ICA) can be successfully exploited for acoustic source localization. As the ICA signal model inherently accounts for the presence of several sources and multiple sound propagation paths, the ICA criterion offers a theoretically more rigorous framework than conventional techniques based on an idealized single-path and single-source signal model. This leads to algorithms which outperform other localization methods, especially in the presence of multiple simultaneously active sound sources and under adverse conditions, notably in reverberant environments. Three methods are investigated to extract the time difference of arrival (TDOA) information contained in the filters of a two-channel broadband ICA scheme. While for the first, the blind system identification (BSI) approach, the number of sources should be restricted to the number of sensors, the other methods, the averaged directivity pattern (ADP) and composite mapped filter (CMF) approaches can be used even when the number of sources exceeds the number of sensors. To allow fast tracking of moving sources, the ICA algorithm operates in block-wise batch mode, with a proportionate weighting of the natural gradient to speed up the convergence of the algorithm. The TDOA estimation accuracy of the proposed schemes is assessed in highly noisy and reverberant environments for two, three, and four stationary noise sources with speech-weighted spectral envelopes as well as for moving real speech sources.
Serotonergic Hallucinogen-Induced Visual Perceptual Alterations.

Serotonergic hallucinogens, such as lysergic acid diethylamide (LSD), psilocybin, and N,N-dimethyltryptamine (DMT), are famous for their capacity to temporally and profoundly alter an individual's visual experiences. These visual alterations show consistent attributes despite large inter- and intra-individual variances. Many reports document a common perception of colors as more saturated, with increased brightness and contrast in the environment ("Visual Intensifications"). Environmental objects might be altered in size ("Visual illusions") or take on a modified and special meaning for the subject ("Altered self-reference"). Subjects may perceive light flashes or geometrical figures containing recurrent patterns ("Elementary imagery and hallucinations") influenced by auditory stimuli ("Audiovisual synesthesia"), or they may envision images of people, animals, or landscapes ("Complex imagery and hallucinations") without any physical stimuli supporting their percepts. This wide assortment of visual phenomena suggests that one single neuropsychopharmacological mechanism is unlikely to explain such vast phenomenological diversity. Starting with mechanisms that act at the cellular level, the key role of 5-HT2A receptor activation and the subsequent increased cortical excitation will be considered. Next, it will be shown that area specific anatomical and dynamical features link increased excitation to the specific visual contents of hallucinations. The decrease of alpha oscillations by hallucinogens will then be introduced as a systemic mechanism for amplifying internal-driven excitation that overwhelms stimulus-induced excitations. Finally, the hallucinogen-induced parallel decrease of the N170 visual evoked potential and increased medial P1 potential will be discussed as key mechanisms for inducing a dysbalance between global integration and early visual gain that may explain several hallucinogen-induced visual experiences, including visual hallucinations, illusions, and intensifications.
A bioinspired omniphobic surface coating on medical devices prevents thrombosis and biofouling

Thrombosis and biofouling of extracorporeal circuits and indwelling medical devices cause significant morbidity and mortality worldwide. We apply a bioinspired, omniphobic coating to tubing and catheters and show that it completely repels blood and suppresses biofilm formation. The coating is a covalently tethered, flexible molecular layer of perfluorocarbon, which holds a thin liquid film of medical-grade perfluorocarbon on the surface. This coating prevents fibrin attachment, reduces platelet adhesion and activation, suppresses biofilm formation and is stable under blood flow in vitro. Surface-coated medical-grade tubing and catheters, assembled into arteriovenous shunts and implanted in pigs, remain patent for at least 8 h without anticoagulation. This surface-coating technology could reduce the use of anticoagulants in patients and help to prevent thrombotic occlusion and biofouling of medical devices.