nils-herrmann/clean_scirepeval huggingface dataset:
Questions in, knowledge in?: a study of naver's question answering community

Large general-purposed community question-answering sites are becoming popular as a new venue for generating knowledge and helping users in their information needs. In this paper we analyze the characteristics of knowledge generation and user participation behavior in the largest question-answering online community in South Korea, Naver Knowledge-iN. We collected and analyzed over 2.6 million question/answer pairs from fifteen categories between 2002 and 2007, and have interviewed twenty six users to gain insights into their motivations,roles, usage and expertise. We find altruism, learning, and competency are frequent motivations for top answerers to participate, but that participation is often highly intermittent. Using a simple measure of user performance, we find that higher levels of participation correlate with better performance. We also observe that users are motivated in part through a point system to build a comprehensive knowledge database. These and other insights have significant implications for future knowledge generating online communities.
Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers

Low-rank modeling has many important applications in computer vision and machine learning. While the matrix rank is often approximated by the convex nuclear norm, the use of nonconvex low-rank regularizers has demonstrated better empirical performance. However, the resulting optimization problem is much more challenging. Recent state-of-the-art requires an expensive full SVD in each iteration. In this paper, we show that for many commonly-used nonconvex low-rank regularizers, the singular values obtained from the proximal operator can be automatically threshold. This allows the proximal operator to be efficiently approximated by the power method. We then develop a fast proximal algorithm and its accelerated variant with inexact proximal step. It can be guaranteed that the squared distance between consecutive iterates converges at a rate of , where is the number of iterations. Furthermore, we show the proposed algorithm can be parallelized, and the resultant algorithm achieves nearly linear speedup w.r.t. the number of threads. Extensive experiments are performed on matrix completion and robust principal component analysis. Significant speedup over the state-of-the-art is observed.
Modeling and Implementation of Space Vector PWM Driver of 3-Phase Induction Motor

This research proposes the design, simulation, and implementation of a three-phase induction motor driver, using voltage-fed Space Vector Pulse Width Modulation technique (SVPWM), which is an advance and modern technique. The SVPWM provides maximum usage of the DC link. A MATLAB/SIMULINK program is prepared for simulating the overall drive system which include; voltage-fed space vector PWM inverter model and three-phase induction motor model. A practical model is designed by imitate the conceptions of TMS320 (DSP) microcontroller. This practical model is completely implemented and exact results are obtained. The worst state of the harmonics content of the voltage and current (no-load condition) are analyzed. This analysis shows high reduction in the dominant harmonics and very low total harmonic distortion (THD) when SVPWM is used (less than 5%), compared to (more than 20%) in square wave. Experimental and simulation results have verified the superior performance and the effectiveness in reduction the harmonic losses and switching losses.
Virtual Reality and Augmented Reality in Education

Virtual reality and augmented reality technology has existed in various forms for over two decades. However, high cost proved to be one of the main barriers to its adoption in education, outside of experimental studies. The creation and widespread sale of low-cost virtual reality devices using smart phones has made virtual reality technology available to the common person. This paper reviews how virtual reality and augmented reality has been used in education, discusses the advantages and disadvantages of using these technologies in the classroom, and describes how virtual reality and augmented reality technologies can be used to enhance teaching at the United States Military Academy.
Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination

An affine invariant representation is constructed with a cascade of invariants, which preserves information for classification. A joint translation and rotation invariant representation of image patches is calculated with a scattering transform. It is implemented with a deep convolution network, which computes successive wavelet transforms and modulus non-linearities. Invariants to scaling, shearing and small deformations are calculated with linear operators in the scattering domain. State-of-the-art classification results are obtained over texture databases with uncontrolled viewing conditions.
Single color extraction and image query

In this paper we propose a method for automatic color extraction and indexing to support color queries of image and video databases. This approach identifies the regions within images that contain colors from predetermined color sets. By searching over a large number of color sets, a color index for the database is created in a fashion similar to that for file inversion. This allows very fast indexing of the image collection by color contents of the images. Furthermore, information about the identified regions, such as the color set, size, and location, enables a rich variety of queries that specify both color content and spatial relationships of regions. We present the single color extraction and indexing method and contrast it to other color approaches. We examine single and multiple color extraction and image query on a database of 3000 color images.
Extracting search-focused key n-grams for relevance ranking in web search

In web search, relevance ranking of popular pages is relatively easy, because of the inclusion of strong signals such as anchor text and search log data. In contrast, with less popular pages, relevance ranking becomes very challenging due to a lack of information. In this paper the former is referred to as head pages, and the latter tail pages. We address the challenge by learning a model that can extract search-focused key n-grams from web pages, and using the key n-grams for searches of the pages, particularly, the tail pages. To the best of our knowledge, this problem has not been previously studied. Our approach has four characteristics. First, key n-grams are search-focused in the sense that they are defined as those which can compose "good queries" for searching the page. Second, key n-grams are learned in a relative sense using learning to rank techniques. Third, key n-grams are learned using search log data, such that the characteristics of key n-grams in the search log data, particularly in the heads; can be applied to the other data, particularly to the tails. Fourth, the extracted key n-grams are used as features of the relevance ranking model also trained with learning to rank techniques. Experiments validate the effectiveness of the proposed approach with large-scale web search datasets. The results show that our approach can significantly improve relevance ranking performance on both heads and tails; and particularly tails, compared with baseline approaches. Characteristics of our approach have also been fully investigated through comprehensive experiments.
Invited: Cross-layer approximate computing: From logic to architectures

We present a survey of approximate techniques and discuss concepts for building power-/energy-efficient computing components reaching from approximate accelerators to arithmetic blocks (like adders and multipliers). We provide a systematical understanding of how to generate and explore the design space of approximate components, which enables a wide-range of power/energy, performance, area and output quality tradeoffs, and a high degree of design flexibility to facilitate their design. To enable cross-layer approximate computing, bridging the gap between the logic layer (i.e. arithmetic blocks) and the architecture layer (and even considering the software layers) is crucial. Towards this end, this paper introduces open-source libraries of low-power and high-performance approximate components. The elementary approximate arithmetic blocks (adder and multiplier) are used to develop multi-bit approximate arithmetic blocks and accelerators. An analysis of data-driven resilience and error propagation is discussed. The approximate computing components are a first steps towards a systematic approach to introduce approximate computing paradigms at all levels of abstractions.
A large database of graphs and its use for benchmarking graph isomorphism algorithms

Despite of the fact that graph-based methods are gaining more and more popularity in different scientific areas, it has to be considered that the choice of an appropriate algorithm for a given application is still the most crucial task. The lack of a large database of graphs makes the task of comparing the performance of different graph matching algorithms difficult, and often the selection of an algorithm is made on the basis of a few experimental results available. In this paper we present an experimental comparative evaluation of the performance of four graph matching algorithms. In order to perform this comparison, we have built and made available a large database of graphs, which is also described in detail in this article.
Variable stiffness fabrics with embedded shape memory materials for wearable applications

Materials with variable stiffness have the potential to provide a range of new functionalities, including system reconfiguration by tuning the location of rigid links and joints. In particular, wearable applications would benefit from variable stiffness materials in the context of active braces that may stiffen when necessary and soften when mobility is required. In this work, we present fibers capable of adjusting to provide variable stiffness in wearable fabrics. The variable stiffness fibers are made from shape memory materials, where shape memory alloy (SMA) is coated with a thin film of shape memory polymer (SMP). The fibers, which are fabricated via a continuous feed-through process, reduce in bending stiffness by an order of magnitude when the SMP goes through the glass transition. The transition between rubbery and glassy state is accomplished by direct joule heating of the embedded SMA wire. We employ a COMSOL model to relate the current input to the time required for the fibers to transition between stiffness states. Finally, we demonstrate how this device can be worn and act as a joint stability brace on human fingers.
Social media use and potential in business-to-business companies' innovation

Social media use, potential and challenges in innovation have received little attention in literature, especially from the standpoint of the business-to-business sector. Therefore, this paper focuses on bridging this gap with a survey of social media use, potential and challenges, combined with a social media - focused innovation literature review of state-of-the-art. The study also studies the essential differences between business-to-consumer and business-to-business in the above respects. The paper starts by defining of social media and web 2.0, and then characterizes social media in business, social media in business-to-business sector and social media in business-to-business innovation. Finally we present and analyze the results of our empirical survey of 122 Finnish companies. This paper suggests that there is a significant gap between perceived potential of social media and social media use in innovation activity in business-to-business companies, recognizes potentially effective ways to reduce the gap, and clarifies the found differences between B2B's and B2C's.
IntentNet: Learning to Predict Intention from Raw Sensor Data

In order to plan a safe maneuver, self-driving vehicles need to understand the intent of other traffic participants. We define intent as a combination of discrete high level behaviors as well as continuous trajectories describing future motion. In this paper we develop a one-stage detector and forecaster that exploits both 3D point clouds produced by a LiDAR sensor as well as dynamic maps of the environment. Our multi-task model achieves better accuracy than the respective separate modules while saving computation, which is critical to reduce reaction time in self-driving applications.
Self-substrate-triggered technique to enhance turn-on uniformity of multi-finger ESD protection devices

A novel self-substrate-triggered (SST) technique is proposed to solve the nonuniform turn-on issue of the multi-finger GGNMOS for ESD protection. The first turned-on center finger is used to trigger on all fingers in the GGNMOS structure with self-substrate-triggered technique. So, the turn-on uniformity and ESD robustness of GGNMOS can be greatly improved by the new proposed self-substrate-triggered technique.
Facial symmetry and the perception of beauty

The question of what makes a face attractive, and whether our preferences come from culture or biology, has fascinated scholars for centuries. Variation in the ideals of beauty across societies and historical periods suggests that standards of beauty are set by cultural convention. Recent evidence challenges this view, however, with infants as young as 2 months of age preferring to look at faces that adults find attractive (Langlois et al., 1987), and people from different cultures showing considerable agreement about which faces are attractive (Cun-for a review). These findings raise the possibility that some standards of beauty may be set by nature rather than culture. Consistent with this view, specific preferences have been identified that appear to be part of our biological rather than Such a preference would be adaptive if stabilizing selection operates on facial traits (Symons, 1979), or if averageness is associated with resistance to pathogens , as some have suggested Evolutionary biologists have proposed that a preference for symmetry would also be adaptive because symmetry is a signal of health and genetic quality Only high-quality individuals can maintain symmetric development in the face of environmental and genetic stresses. Symmetric bodies are certainly attractive to humans and many other animals but what about symmetric faces? Biologists suggest that facial symmetry should be attractive because it may signal mate quality High levels of facial asymmetry in individuals with chro-mosomal abnormalities (e.g., Down's syndrome and Tri-somy 14; for a review, see Thornhill & Møller, 1997) are consistent with this view, as is recent evidence that facial symmetry levels correlate with emotional and psychological health (Shackelford & Larsen, 1997). In this paper, we investigate whether people can detect subtle differences in facial symmetry and whether these differences are associated with differences in perceived attractiveness. Recently, Kowner (1996) has reported that faces with normal levels of asymmetry are more attractive than perfectly symmetric versions of the same faces. 3 Similar results have been reported by Langlois et al. and an anonymous reviewer for helpful comments on an earlier version of the manuscript. We also thank Graham Byatt for assistance with stimulus construction, Linda Jeffery for assistance with the figures, and Alison Clark and Catherine Hickford for assistance with data collection and statistical analysis in Experiment 1A. Evolutionary, as well as cultural, pressures may contribute to our perceptions of facial attractiveness. Biologists predict that facial symmetry should be attractive, because it may signal …
Fast and robust multi-atlas segmentation of brain magnetic resonance images

We introduce an optimised pipeline for multi-atlas brain MRI segmentation. Both accuracy and speed of segmentation are considered. We study different similarity measures used in non-rigid registration. We show that intensity differences for intensity normalised images can be used instead of standard normalised mutual information in registration without compromising the accuracy but leading to threefold decrease in the computation time. We study and validate also different methods for atlas selection. Finally, we propose two new approaches for combining multi-atlas segmentation and intensity modelling based on segmentation using expectation maximisation (EM) and optimisation via graph cuts. The segmentation pipeline is evaluated with two data cohorts: IBSR data (N=18, six subcortial structures: thalamus, caudate, putamen, pallidum, hippocampus, amygdala) and ADNI data (N=60, hippocampus). The average similarity index between automatically and manually generated volumes was 0.849 (IBSR, six subcortical structures) and 0.880 (ADNI, hippocampus). The correlation coefficient for hippocampal volumes was 0.95 with the ADNI data. The computation time using a standard multicore PC computer was about 3-4 min. Our results compare favourably with other recently published results.
Ground Truth for Grammaticality Correction Metrics

How do we know which grammatical error correction (GEC) system is best? A number of metrics have been proposed over the years, each motivated by weaknesses of previous metrics; however, the metrics themselves have not been compared to an empirical gold standard grounded in human judgments. We conducted the first human evaluation of GEC system outputs, and show that the rankings produced by metrics such as MaxMatch and I-measure do not correlate well with this ground truth. As a step towards better metrics, we also propose GLEU, a simple variant of BLEU, modified to account for both the source and the reference, and show that it hews much more closely to human judgments.
Recognizing Image Style

The style of an image plays a significant role in how it is viewed, but style has received little attention in computer vision research. We describe an approach to predicting style of images, and perform a thorough evaluation of different image features for these tasks. We find that features learned in a multi-layer network generally perform best – even when trained with object class (not style) labels. Our large-scale learning methods results in the best published performance on an existing dataset of aesthetic ratings and photographic style annotations. We present two novel datasets: 80K Flickr photographs annotated with 20 curated style labels, and 85K paintings annotated with 25 style/genre labels. Our approach shows excellent classification performance on both datasets. We use the learned classifiers to extend traditional tag-based image search to consider stylistic constraints, and demonstrate cross-dataset understanding of style.
Incremental Coordination: Attention-Centric Speech Production in a Physically Situated Conversational Agent

Inspired by studies of human-human conversations, we present methods for incrementally coordinating speech production with listeners’ visual foci of attention. We introduce a model that considers the demands and availability of listeners’ attention at the onset and throughout the production of system utterances, and that incrementally coordinates speech synthesis with the listener’s gaze. We present an implementation and deployment of the model in a physically situated dialog system and discuss lessons learned.
Camera Pose and Calibration from 4 or 5 Known 3D Points

We describe two direct quasilinear methods for camera pose (absolute orientation) and calibration from a single image of 4 or 5 known 3D points. They generalize the 6 point ‘Direct Linear Transform’ method by incorporating partial prior camera knowledge, while still allowing some unknown calibration parameters to be recovered. Only linear algebra is required, the solution is unique in non-degenerate cases, and additional points can be included for improved stability. Both methods fail for coplanar points, but we give an experimental eigendecomposition based one that handles both planar and nonplanar cases. Our methods use recent polynomial solving technology, and we give a brief summary of this. One of our aims was to try to understand the numerical behaviour of modern polynomial solvers on some relatively simple test cases, with a view to other vision applications.
360-degree Visual Detection and Target Tracking on an Autonomous Surface Vehicle

This paper describes perception and planning systems of an autonomous sea surface vehicle (ASV) whose goal is to detect and track other vessels at medium to long ranges and execute responses to determine whether the vessel is adversarial. The Jet Propulsion Laboratory (JPL) has developed a tightly integrated system called CARACaS (Control Architecture for Robotic Agent Command and Sensing) that blends the sensing, planning, and behavior autonomy necessary for such missions. Two patrol scenarios are addressed here: one in which the ASV patrols a large harbor region and checks for vessels near a fixed asset on each pass and one in which the ASV circles a fixed asset and intercepts approaching vessels. This paper focuses on the ASV’s central perception and situation awareness system, dubbed Surface Autonomous Visual Analysis and Tracking (SAVAnT), which receives images from an omnidirectional camera head, identifies objects of interest in these images, and probabilistically tracks the objects’ presence over time, even as they may exist outside of the vehicle’s sensor range. The integrated CARACaS/SAVAnT system has been implemented on U.S. Navy experimental ASVs and tested in on-water field demonstrations.
Support Vector Data Description

Data domain description concerns the characterization of a data set. A good description covers all target data but includes no superfluous space. The boundary of a dataset can be used to detect novel data or outliers. We will present the Support Vector Data Description (SVDD) which is inspired by the Support Vector Classifier. It obtains a spherically shaped boundary around a dataset and analogous to the Support Vector Classifier it can be made flexible by using other kernel functions. The method is made robust against outliers in the training set and is capable of tightening the description by using negative examples. We show characteristics of the Support Vector Data Descriptions using artificial and real data.
SUMMA. A Robust and Adaptable Summarization Tool

Over the last few years there has been substantial research on text summarization, but comparatively little research has been carried out on adaptable components that allow rapid development and evaluation of summarization solutions. This paper presents a set of adaptable summarization components together with well-established evaluation tools, all within the GATE paradigm. The toolkit includes resources for the computation of summarization features which are combined in order to provide functionalities for single-document, multi-document, querybased, and multi/cross-lingual summarization. The summarization tools have been successfully used in a number of applications including a fully-fledged information access system. RÉSUMÉ. Au cours des dernières années il y a eu un nombre important de recherches au sujet du résumé automatique. Toutefois, il y a eu comparativement peu de recherche au sujet des ressources computationnelles et composantes qui peuvent être adaptées facilement pour le développement et l’évaluation des systèmes de résumé automatique. Ici on présente un ensemble de ressources spécifiquement développées pour le résumé automatique qui se basent sur la plateforme GATE. Les composantes sont utilisées pour calculer des traits indiquant la pertinence des phrases. Ces composantes sont combinées pour produire différents types de systèmes de résumé tels que résumé de document simple, résumé de document multiple, et résumé basé sur des topiques. Les ressources et algorithmes implémentés ont été utilisés pour développer plusieurs applications pour l’accès à l’information dans des systèmes d’information.