nils-herrmann/clean_scirepeval huggingface dataset:
Fiber-Reinforced Polymer Composites for Construction — State-ofthe-Art Review

A concise state-of-the-art survey of fiber-reinforced polymer ~also known as fiber-reinforced plastic ! composites for construc tion applications in civil engineering is presented. The paper is organized into separate sections on structural shapes, bridge dec reinforcements, externally bonded reinforcements, and standards and codes. Each section includes a historical review, the curr the art, and future challenges.  10.1061/ ~ASCE!1090-0268~2002!6:2~73! CE Database keywords: Composite materials; Fiber-reinforced materials; Polymers; Bridge decks; State-of-the-art reviews. rials sive uilt , th To So-
A selective overview of feature screening for ultrahigh-dimensional data.

High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of high-dimensional data poses many challenges for statisticians. Feature selection and variable selection are fundamental for high-dimensional data analysis. The sparsity principle, which assumes that only a small number of predictors contribute to the response, is frequently adopted and deemed useful in the analysis of high-dimensional data. Following this general principle, a large number of variable selection approaches via penalized least squares or likelihood have been developed in the recent literature to estimate a sparse model and select significant variables simultaneously. While the penalized variable selection methods have been successfully applied in many high-dimensional analyses, modern applications in areas such as genomics and proteomics push the dimensionality of data to an even larger scale, where the dimension of data may grow exponentially with the sample size. This has been called ultrahigh-dimensional data in the literature. This work aims to present a selective overview of feature screening procedures for ultrahigh-dimensional data. We focus on insights into how to construct marginal utilities for feature screening on specific models and motivation for the need of model-free feature screening procedures.
Maxillary length at 11-14 weeks of gestation in fetuses with trisomy 21.

To determine the value of measuring maxillary length at 11-14 weeks of gestation in screening for trisomy 21.



In 970 fetuses ultrasound examination was carried out for measurement of crown-rump length (CRL), nuchal translucency and maxillary length, and to determine if the nasal bone was present or absent, immediately before chorionic villus sampling for karyotyping at 11-14 weeks of gestation. In 60 cases the maxillary length was measured twice by the same operator to calculate the intraobserver variation in measurements.



The median gestation was 12 (range, 11-14) weeks. The maxilla was successfully examined in all cases. The mean difference between paired measurements of maxillary length was -0.012 mm and the 95% limits of agreement were -0.42 (95% CI, -0.47 to -0.37) to 0.40 (95% CI, 0.35 to 0.44) mm. The fetal karyotype was normal in 839 pregnancies and abnormal in 131, including 88 cases of trisomy 21. In the chromosomally normal group the maxillary length increased significantly with CRL from a mean of 4.8 mm at a CRL of 45 mm to 8.3 mm at a CRL of 84 mm. In the trisomy 21 fetuses the maxillary length was significantly shorter than normal by 0.7 mm and in the trisomy 21 fetuses with absent nasal bone the maxilla was shorter than in those with present nasal bone by 0.5 mm. In fetuses with other chromosomal defects there were no significant differences from normal in the maxillary length.



At 11-14 weeks of gestation, maxillary length in trisomy 21 fetuses is significantly shorter than in normal fetuses.
Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to solve the problems of uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers’ premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide the first comprehensive survey on IoTaided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems.
The persistent effect of pre-college computing experience on college CS course grades

Many college computer science majors have little or no pre-college computing experience. Previous work has shown that inexperienced students under-perform their experienced peers when placed in the same introductory courses and are more likely to drop out of the CS program. However, not much is known about what, if any, differences may persist beyond the introductory sequence for students who remain in the program. We conducted a study across all levels of a CS program at a large public university in the United States to determine whether grade differences exist between students with and without pre-college experience, and if so, for what types of experiences. We find significant grade differences in courses at all levels of the program. We further find that students who took AP Computer Science receive significantly higher average grades---by up to a half grade---in nearly all courses we studied. Pre-college experience appears to have a weaker relationship with retention and with low-stakes assessment grades. We discuss the limitations of these findings and implications for high school and college level CS courses and programs.
Learning Object Affordances: From Sensory--Motor Coordination to Imitation

Affordances encode relationships between actions, objects, and effects. They play an important role on basic cognitive capabilities such as prediction and planning. We address the problem of learning affordances through the interaction of a robot with the environment, a key step to understand the world properties and develop social skills. We present a general model for learning object affordances using Bayesian networks integrated within a general developmental architecture for social robots. Since learning is based on a probabilistic model, the approach is able to deal with uncertainty, redundancy, and irrelevant information. We demonstrate successful learning in the real world by having an humanoid robot interacting with objects. We illustrate the benefits of the acquired knowledge in imitation games.
The Graph Isomorphism Problem

The graph isomorphism problem can be easily stated: check to see if two graphs that look di erently are actually the same. The problem occupies a rare position in the world of complexity theory, it is clearly in NP but is not known to be in P and it is not known to be NP-complete. Many sub-disciplines of mathematics, such as topology theory and group theory, can be brought to bear on the problem, and yet only for special classes of graphs have polynomial-time algorithms been discovered. Incongruently, this problem seems very easy in practice. It is almost always trivial to check two random graphs for isomorphism, and fast hardware implementations exists for application domains such as image processing. This paper is mostly a survey of related work in the graph isomorphism eld. We examine the problem from many angles, mirroring the multifaceted nature of the literature. We survey complexity results for the graph isomorphism problem, and discuss some of the classes of graphs which have known polynomial-time algorithms. Many of these algorithms have constants that are so large as to negate their usefulness in actual applications. Thus a number of practical techniques, which do not have a polynomial worst case, are also examined. We end the paper by discussing some graphs which are \hard" for practical algorithms, and present experimental results which demonstrate a class of graphs on which practical algorithms run into di culty.
Activities and well-being in older age: effects of self-concept and educational attainment.

The positive effect of activities on well-being is proposed to be mediated by self-conceptualizations and facilitated by socioeconomic status. The hypothesized processes were estimated with LISREL VIII using data from a large cross-sectional survey with a sample of 679 adults aged 65 and older who were representative of older adults living in the Detroit area. Findings indicate that the frequency of performing both leisure and productive activities yields an effect on physical health and depression and that these effects are mediated in part by a sense of self as agentic, but less clearly by a sense of self as social. Furthermore, socioeconomic status, operationalized as formal educational attainment, facilitates the effect of leisure to a greater extent than that of productive activities.
Automatic Forest-Fire Measuring Using Ground Stations and Unmanned Aerial Systems

This paper presents a novel system for automatic forest-fire measurement using cameras distributed at ground stations and mounted on Unmanned Aerial Systems (UAS). It can obtain geometrical measurements of forest fires in real-time such as the location and shape of the fire front, flame height and rate of spread, among others. Measurement of forest fires is a challenging problem that is affected by numerous potential sources of error. The proposed system addresses them by exploiting the complementarities between infrared and visual cameras located at different ground locations together with others onboard Unmanned Aerial Systems (UAS). The system applies image processing and geo-location techniques to obtain forest-fire measurements individually from each camera and then integrates the results from all the cameras using statistical data fusion techniques. The proposed system has been extensively tested and validated in close-to-operational conditions in field fire experiments with controlled safety conditions carried out in Portugal and Spain from 2001 to 2006.
Rules and Ontologies for the Semantic Web

Rules and ontologies play a key role in the layered architecture of the Semantic Web, as they are used to ascribe meaning to, and to reason about, data on the Web. While the Ontology Layer of the Semantic Web is quite developed, and the Web Ontology Language (OWL) is a W3C recommendation since a couple of years already, the rules layer is far less developed and an active area of research; a number of initiatives and proposals have been made so far, but no standard as been released yet. Many implementations of rule engines are around which deal with Semantic Web data in one or another way. This article gives a comprehensive, although not exhaustive, overview of such systems, describes their supported languages, and sets them in relation with theoretical approaches for combining rules and ontologies as foreseen in the Semantic Web architecture. In the course of this, we identify desired properties and common features of rule languages and evaluate existing systems against their support. Furthermore, we review technical problems underlying the integration of rules and ontologies, and classify representative proposals for theoretical integration approaches into different categories.
Annotating Multi-media/Multi-modal Resources with ELAN

This paper shows the actual state of development of the manual annotation tool ELAN. It presents usage requirements from three different groups of users and how one annotation model and a number of generic design principles guided the choices made during the development process of ELAN.  At the Max-Planck-Institute for Psycholinguistics (MPI) software development on annotation tools for the manual annotation of multimedia data has been going on since the early 90’s. Over this decade there have been large changes in enabling technology and insights in the nature of linguistic annotation. Media frameworks for the handling of digital audio and especially digital video files have matured, as has media streaming technology. XML has come to existence and has become highly relevant in a short time. Rendering and input of Unicode characters is now commonplace. Simultaneously, users made experiences with the first generation of video annotation tools and became aware of and got used to these new technologies. From this a new set of requirements arose. Finally, annotation tool builders are better aware of each other’s approaches, annotation models and annotation document formats. Clearly convergence is going on, leading to easier exchange of data between annotation tools. An important role in this process was played by the paper by (Bird & Liberman, 2001) that introduced Annotation Graphs. We are closely watching and trying to participate in standards initiatives, as for example ISO TC37/SC4. The first video annotation tool developed at the MPI was MediaTagger, a QuickTime based application that runs only on pre-OS X Macintoshes. It started as a first attempt to exploit the QuickTime Movie data structure, and especially it’s text tracks, as an informal model for linguistic annotation. Since then several new formal models where made, each one building on the experiences of the previous ones and considering new user requirements. The formal modeling languages that were used are Entity-Relationship diagrams and UML. A detailed presentation and evaluation of these models can be found in (Brugman & Wittenburg, 2001). The next chapters will discuss the requirements of several different groups of users and describe the latest state of ELAN functionality. We will then present our model for annotation in some detail and show how we can cover the needs of very different user groups with one relatively simple model. In the discussions plans for future development will presented. 1 http://www.mpi.nl User requirements ELAN is developed with a number of different user groups in mind. These users are situated both within the MPI and, in an increasing number of cases, outside the MPI. Often they are participating in externally funded projects (DoBeS, ECHO). We will discuss the main requirements per group, although there is of course a substantial overlap between each group’s needs.
Context augmented Dynamic Bayesian Networks for event recognition

This paper proposes a new Probabilistic Graphical Model (PGM) to incorporate the scene, event object interaction, and the event temporal contexts into Dynamic Bayesian Networks (DBNs) for event recognition in surveillance videos. We first construct the baseline event DBNs for modeling the events from their own appearance and kinematic observations, and then augment the DBN with contexts to improve its event recognition performance. Unlike the existing context methods, our model incorporates various contexts simultaneously into one unified model. Experiments on real scene surveillance datasets with complex backgrounds show that the contexts can effectively improve the event recognition performance even under great challenges like large intra-class variations and low image resolution. The topic of modeling and recognizing events in video surveillance system has attracted growing interest from both academia and industry (Oh et al., 2011). Various graphical, syntactic, and description-based approaches (Turaga et al., 2008) have been introduced for modeling and understanding events. Among those approaches, the time-sliced graphical models, i.e. Hidden Markov Models (HMMs) and Dynamic Bayesian Networks (DBNs), have become popular tools. However, surveillance video event recognition still faces difficulties even with the well-built models for describing the events. The first difficulty arises from the tremendous intra-class variations in events. The same category of events can have huge variations in their observations due to visual appearances differences, target motion variations, viewpoint change and temporal variability. Also, the low resolution of event targets also affects event recognition. To compensate for such challenges, we propose to capture various contextual knowledge and systematically integrate them with image data using a Probabilistic Graphical Model (PGM) (Koller and Friedman, 2009) to improve the performance of event recognition on challenging surveillance videos. Contextual knowledge can be regarded as one type of extra information that does not directly describe the recognition task, but it can support the task. As an additional information that can capture certain temporal, spatial or logical relationships with the recognition target, context plays an important role for various visual recognition tasks. Various contexts that are available/retriev-able both during training and testing are widely used in many approaches. For example, Yao and Fei-Fei (2010) and Yao and Fei-Fei (2012) propose a context model to make human pose estimation task and object detection task as mutual context to help each other. Also, Ding et al. (2012) use both the local features and the context cues in the neighborhood windows to construct a combined feature …
Twitter sentiment analysis using deep learning methods

The social media has Immense and popularity among all the services today. Data from SNS (Social Network Service) can be used for a lot of objectives such as prediction or sentiment analysis. Twitter is a SNS that has a huge data with user posting, with this significant amount of data, it has the potential of research related to text mining and could be subjected to sentiment analysis. But handling such a huge amount of unstructured data is a difficult task, machine learning is needed for handling such huge of data. Deep learning is of the machine learning method that use the deep feed forward neural network with many hidden layers in the term of neural network with the result of the experiment about 75%.
X band septum polarizer as feed for parabolic antenna

Contemporary RHCP and LHCP wave occurs in several applications of microwave communication and measurement system. From this point of view the septum polarizer can be useful. The septum polarizer is a four-port waveguide device. The square waveguide at one end constitutes two ports because it can support two orthogonal modes. A slopping (or stepped) septum divides the square waveguide into two standard rectangular waveguides sharing a common broad-wall. The size of the septum as well as two versions of the waveguides excitation were analyzed and are described in this paper.
Minimal intervention dentistry for managing dental caries - a review: report of a FDI task group.

This publication describes the history of minimal intervention dentistry (MID) for managing dental caries and presents evidence for various carious lesion detection devices, for preventive measures, for restorative and non-restorative therapies as well as for repairing rather than replacing defective restorations. It is a follow-up to the FDI World Dental Federation publication on MID, of 2000. The dental profession currently is faced with an enormous task of how to manage the high burden of consequences of the caries process amongst the world population. If it is to manage carious lesion development and its progression, it should move away from the 'surgical' care approach and fully embrace the MID approach. The chance for MID to be successful is thought to be increased tremendously if dental caries is not considered an infectious but instead a behavioural disease with a bacterial component. Controlling the two main carious lesion development related behaviours, i.e. intake and frequency of fermentable sugars, to not more than five times daily and removing/disturbing dental plaque from all tooth surfaces using an effective fluoridated toothpaste twice daily, are the ingredients for reducing the burden of dental caries in many communities in the world. FDI's policy of reducing the need for restorative therapy by placing an even greater emphasis on caries prevention than is currently done, is therefore, worth pursuing.
A Microactuation and Sensing Platform With Active Lockdown for In Situ Calibration of Scale Factor Drifts in Dual-Axis Gyroscopes

This paper presents the design and experimental results of a microvibratory actuation and sensing platform to provide on-chip physical stimulus for in situ calibration of long-term scale factor drifts in multiaxis microelectromechanical systems (MEMS) inertial sensors. The platform consists of a three degrees-of-freedom micromotion stage that can provide piezoelectric actuation for X/Y-tilting reference stimuli, compensation of undesired off-axis motion, integrated sensing of applied periodic stimulus, and electrostatic position lock-down for shock protection. A dual-axis MEMS gyroscope is mounted on top of the microplatform, and its electrical interconnects are provided through microfabricated highly flexible parylene cables with virtually zero-loading. The piezoelectric stage is measured to provide up to 280°/s angular ac excitation to a 25-mg inertial sensor payload at an expense of <;100 μW, while providing an analog sensing signal (11 mV/°/s) to determine the applied rate with a precision of 1.2 °/s. The estimated scale factor has <; 0.8% deviation from rate-table characterized values on the same-model gyroscope samples. With further improvements in control precision and angular velocity estimation, the introduced platform is expected enable on-chip self-calibration of long-term scale-factor drifts to <; 100 ppm.
Visualizing Time-Dependent Data Using Dynamic t-SNE

Many interesting processes can be represented as time-dependent datasets. We define a time-dependent dataset as a sequence of datasets captured at particular time steps. In such a sequence, each dataset is composed of observations (high-dimensional real vectors), and each observation has a corresponding observation across time steps. Dimensionality reduction provides a scalable alternative to create visualizations (projections) that enable insight into the structure of such datasets. However, applying dimensionality reduction independently for each dataset in a sequence may introduce unnecessary variability in the resulting sequence of projections, which makes tracking the evolution of the data significantly more challenging. We show that this issue affects t-SNE, a widely used dimensionality reduction technique. In this context, we propose dynamic t-SNE, an adaptation of t-SNE that introduces a controllable trade-off between temporal coherence and projection reliability. Our evaluation in two time-dependent datasets shows that dynamic t-SNE eliminates unnecessary temporal variability and encourages smooth changes between projections.
Segmental Pattern Discovery in Music

I this paper we describe a new method for discovering recurrent patterns in a corpus of segmented melodies. Elements of patterns in this scheme do not represent individual notes but rather represent melodic segments that are sequences of notes. A new knowledge representation for segmental patterns is designed, and a pattern discovery algorithm based on suffix trees is used to discover segmental patterns in large corpora. The method is applied to a large collection of melodies, including Nova Scotia folk songs, Bach chorale melodies, and sections from the Essen folk song database. Patterns are ranked using a statistical significance method that integrates pattern self-overlap, length, and frequency in a corpus into a single measure. A musical interpretation of some of the statistically significant discovered patterns is presented.