nils-herrmann/clean_scirepeval huggingface dataset:
Bispectrum estimation: A digital signal processing framework

It is the purpose of this tutorial paper to place bispectrum estimation in a digital signal processing framework in order to aid engineers in grasping the utility of the available bispectrum estimation techniques, to discuss application problems that can directly benefit from the use of the bispectrum, and to motivate research in this area. Three general reasons are behind the use of bispectrum in signal processing and are addressed in the paper: to extract information due to deviations from normality, to estimate the phase of parametric signals, and to detect and characterize the properties of nonlinear mechanisms that generate time series.
Ontology and Information Systems

Ontology as a branch of philosophy is the science of what is, of the kinds and structures of objects, properties, events, processes and relations in every area of reality. ‘Ontology’ is often used by philosophers as a synonym for ‘metaphysics’ (literally: ‘what comes after the Physics’), a term which was used by early students of Aristotle to refer to what Aristotle himself called ‘first philosophy’. The term ‘ontology’ (or ontologia) was itself coined in 1613, independently, by two philosophers, Rudolf Göckel (Goclenius), in his Lexicon philosophicum and Jacob Lorhard (Lorhardus), in his Theatrum philosophicum. The first occurrence in English recorded by the OED appears in Bailey’s dictionary of 1721, which defines ontology as ‘an Account of being in the Abstract’.
Characteristics of Underground Channel for Wireless Underground Sensor Networks

Wireless Underground Sensor Networks (WUSN) constitute one of the promising application areas of the recently developed wireless sensor networking techniques. The main difference between WUSN and the terrestrial wireless sensor networks is the communication medium. The propagation characteristics of electromagnetic (EM) waves in soil and the significant differences between propagation in air prevent a straightforward characterization of the underground wireless channel. To this end, in this paper, advanced channel models are proposed to completely characterize the underground wireless channel and lay out the foundations for efficient communication in this environment. In particular, the underground communication channel is modeled considering not only the propagation of EM waves in soil, but also other affects such as multipath, soil composition, water content, and burial depth. The propagation characteristics are shown through simulation results of path loss between two underground sensors. Moreover, based on the proposed channel model, the resulting bit error rate is analyzed for different network and soil parameters. The theoretical analysis and the simulation results prove the feasibility of wireless communication in underground environment and highlight several important aspects in this field. This work will lead to the provision of a generic framework for underground wireless communication and the realization of WUSN.
A pair-wise offset algorithm for 2D point-sequence curve

Presented in the paper is an efficient pair-wise offset algorithm for closed 2D point-sequence curves (PS-curve). A key feature of the proposed algorithm is that all local invalid loopsare removed from the input PS-curve before constructing a raw offset-curve, by invoking a pair-wise interference-detection (PWID) test. In the PWID test, each pair of elementary offset segments is tested for interference and then interfering segments are successively removed. The proposed algorithm has been implemented and tested with various PS-curves. Empirical tests show that the proposed PS-curve offsetting method is very fast and robust with a near O(n) time-complexity, wheren is the number of points in a PS-curve. q 1999 Elsevier
AVA: A large-scale database for aesthetic visual analysis

With the ever-expanding volume of visual content available, the ability to organize and navigate such content by aesthetic preference is becoming increasingly important. While still in its nascent stage, research into computational models of aesthetic preference already shows great potential. However, to advance research, realistic, diverse and challenging databases are needed. To this end, we introduce a new large-scale database for conducting Aesthetic Visual Analysis: AVA. It contains over 250,000 images along with a rich variety of meta-data including a large number of aesthetic scores for each image, semantic labels for over 60 categories as well as labels related to photographic style. We show the advantages of AVA with respect to existing databases in terms of scale, diversity, and heterogeneity of annotations. We then describe several key insights into aesthetic preference afforded by AVA. Finally, we demonstrate, through three applications, how the large scale of AVA can be leveraged to improve performance on existing preference tasks.
Three-dimensional bin packing problem with variable bin height

0377-2217/$ see front matter 2009  A doi:10.1016/j.ejor.2009.05.040 * Corresponding author.  Institute for Logi fax: +61 3 9689 4859. E-mail addresses: wuyong@pmail.ntu.edu.sg (Y. W This paper studies a variant of the three-dimensional bin packing problem (3D-BPP), where the bin height can be adjusted to the cartons it packs. The bins and cartons to be packed are assumed rectangular in shape. The cartons are allowed to be rotated into any one of the six positions that keep the carton edges parallel to the bin edges. This greatly increases the difficulty of finding a good solution since the search space expands significantly comparing to the 3D-BPP where the cartons have fixed orientations. A mathematical (mixed integer programming) approach is modified based on [Chen, C. S., Lee, S. M., Shen, Q. S., 1995. An analytical model for the container loading problem. ] and numerical experiments indicate that the mathematical approach is not suitable for the variable bin height 3D-BPP. A special bin packing algorithm based on packing index is designed to utilize the special problem feature and is used as a building block for a genetic algorithm designed for the 3DBPP. The paper also investigates the situation where more than one type of bin are used and provides a heuristic for packing a batch of cartons using the genetic algorithm. Numerical experiments show that our proposed method yields quick and satisfactory results when benchmarked against the actual packing practice and the MIP model with the latest version of CPLEX.
Multi-modal Score Fusion and Decision Trees for Explainable Automatic Job Candidate Screening from Video CVs

We describe an end-to-end system for explainable automatic job candidate screening from video CVs. In this application, audio, face and scene features are first computed from an input video CV, using rich feature sets. These multiple modalities are fed into modality-specific regressors to predict apparent personality traits and a variable that predicts whether the subject will be invited to the interview. The base learners are stacked to an ensemble of decision trees to produce the outputs of the quantitative stage, and a single decision tree, combined with a rule-based algorithm produces interview decision explanations based on the quantitative results. The proposed system in this work ranks first in both quantitative and qualitative stages of the CVPR 2017 ChaLearn Job Candidate Screening Coopetition.
A New Dataset for Fine-Grained Citation Field Extraction

Citation field extraction entails segmenting a citation string into its constituent parts, such as title, authors, publisher and year. Despite the importance of this task, there is a lack of well-annotated citation data. This paper presents a new labeled dataset for citation extraction that, in comparison to the previous standard dataset, exceeds four-times more data, supplies detailed nested labels rather than coarse-grained flat labels, and is derived from four different academic fields rather than one. We describe our new dataset in detail, and provide baseline experimental results from a state-of-the-art extraction method.
A review of recent advances in visual speech decoding

Visual speech information plays an important role in automatic speech recognition (ASR) especially when audio is corrupted or even inaccessible. Despite the success of audio-based ASR, the problem of visual speech decoding remains widely open. This paper provides a detailed review of recent advances in this research area. In comparison with the previous survey  which covers the whole ASR system that uses visual speech information, we focus on the important questions asked by researchers and summarize the recent studies that attempt to answer them. In particular, there are three questions related to the extraction of visual features, concerning speaker dependency, pose variation and temporal information, respectively. Another question is about audio-visual speech fusion, considering the dynamic changes of modality reliabilities encountered in practice. In addition, the state-of-the-art on facial landmark localization is briefly introduced in this paper. Those advanced techniques can be used to improve the region-of-interest detection, but have been largely ignored when building a visual-based ASR system. We also provide details of audio-visual speech databases. Finally, we discuss the remaining challenges and offer our insights into the future research on visual speech decoding.
Improving Machine Learning Approaches to Coreference Resolution

Wepresentanounphrasecoreferencesystem that extends the work of Soon et al. (2001) and, to our knowledge, producesthebestresultsto dateontheMUC6 andMUC-7 coreferenceresolutiondata sets— F-measuresof 70.4 and63.4, respecti vely. Improvementsarisefrom two sources: extra-linguistic changesto the learningframework anda large-scaleexpansionof thefeaturesetto includemore sophisticatedlinguisticknowledge.
Ask the Mutants: Mutating Faulty Programs for Fault Localization

We present MUSE (MUtation-baSEd fault localization technique), a new fault localization technique based on mutation analysis. A key idea of MUSE is to identify a faulty statement by utilizing different characteristics of two groups of mutants-one that mutates a faulty statement and the other that mutates a correct statement. We also propose a new evaluation metric for fault localization techniques based on information theory, called Locality Information Loss (LIL): it can measure the aptitude of a localization technique for automated fault repair systems as well as human debuggers. The empirical evaluation using 14 faulty versions of the five real-world programs shows that MUSE localizes a fault after reviewing 7.4 statements on average, which is about 25 times more precise than the state-of-the-art SBFL technique Op2.
Largest inscribed rectangles in convex polygons

We consider approximation algorithms for the problem of computing an inscribed rectangle having largest area in a convex polygon on n vertices. If the order of the vertices of the polygon is given, we present a randomized algorithm that computes an inscribed rectangle with area at least (1− ) times the optimum with probability t in time O( 1 log n) for any constant t < 1. We further give a deterministic approximation algorithm that computes an inscribed rectangle of area at least (1− ) times the optimum in running time O( 1 2 log n) and show how this running time can be slightly improved.
Delay-Dependent $\hbox{H}_{\infty }$ Filter Design for Discrete-Time Fuzzy Systems With Time-Varying Delays

This paper investigates delay-dependent Hinfin filter design problems for discrete-time fuzzy systems with time-varying delays. First, a novel delay-dependent piecewise Lyapunov-Krasovskii functional (DDPLKF) is proposed in which both the upper bound of delays and the delay interval are considered. Based on this DDPLKF, the delay-dependent stability criteria for discrete-time systems with constant or time-varying delays are obtained, respectively. Then, delay-dependent full-order and reduced-order Hinfin filter design approaches are proposed. The filter parameters can be obtained by solving a set of linear matrix inequalities (LMIs). Simulation examples are also given to illustrate the performance of the proposed approaches. It is shown that our approaches are less conservative and that the corresponding Hinfin filters can achieve better performance than the existing approaches.
Improving the performance of the MPPT for thermoelectric generator system by using Kalman filter

In this paper, perturb and observe (P&O) algorithm is presented to grab the maximum power point from Thermoelectric Generator (TEG) sources. The main contribution of this study relies on estimating the output power of TEG by using Kalman filter to readjust the characteristics of TEG with respect to operating conditions. In the real applications, there is a disturbance appears on the power curve due to the heat distribution on the TEG surfaces. For smooth implementation of the maximum power point tracking (MPPT) algorithm and to overcome the disturbance, Kalman filter is used. It is estimate the output power curve which paves the way for MPPT to be implemented easily in the microcontroller. The feasibility of the proposed harvesting system verified experimentally.
Does print size matter for reading? A review of findings from vision science and typography.

The size and shape of printed symbols determine the legibility of text. In this paper, we focus on print size because of its crucial role in understanding reading performance and its significance in the history and contemporary practice of typography. We present evidence supporting the hypothesis that the distribution of print sizes in historical and contemporary publications falls within the psychophysically defined range of fluent print size--the range over which text can be read at maximum speed. The fluent range extends over a factor of 10 in angular print size (x-height) from approximately 0.2° to 2°. Assuming a standard reading distance of 40 cm (16 inches), the corresponding physical x-heights are 1.4 mm (4 points) and 14 mm (40 points). We provide new data on the distributions of print sizes in published books and newspapers and in typefounders' specimens, and consider factors influencing these distributions. We discuss theoretical concepts from vision science concerning visual size coding that help inform our understanding of historical and modern typographical practices. While economic, social, technological, and artistic factors influence type design and selection, we conclude that properties of human visual processing play a dominant role in constraining the distribution of print sizes in common use.
Food recognition using statistics of pairwise local features

Food recognition is difficult because food items are de-formable objects that exhibit significant variations in appearance. We believe the key to recognizing food is to exploit the spatial relationships between different ingredients (such as meat and bread in a sandwich). We propose a new representation for food items that calculates pairwise statistics between local features computed over a soft pixel-level segmentation of the image into eight ingredient types. We accumulate these statistics in a multi-dimensional histogram, which is then used as a feature vector for a discriminative classifier. Our experiments show that the proposed representation is significantly more accurate at identifying food than existing methods.
Automatic Capacity Tuning of Very Large VC-Dimension Classifiers

Large VC-dimension classifiers can learn difficult tasks, but are usually impractical because they generalize well only if they are trained with huge quantities of data. In this paper we show that even high-order polynomial classifiers in high dimensional spaces can be trained with a small amount of training data and yet generalize better than classifiers with a smaller VC-dimension. This is achieved with a maximum margin algorithm (the Generalized Portrait). The technique is applicable to a wide variety of classifiers, including Perceptrons, polynomial classifiers (sigma-pi unit networks) and Radial Basis Functions. The effective number of parameters is adjusted automatically by the training algorithm to match the complexity of the problem. It is shown to equal the number of those training patterns which are closest patterns to the decision boundary (supporting patterns). Bounds on the generalization error and the speed of convergence of the algorithm are given. Experimental results on handwritten digit recognition demonstrate good generalization compared to other algorithms.
Colorimetric Method for Determination of Sugars and Related Substances

Simple sugars, oligosaccharides, polysaccharides, and their derivatives, including the methyl ethers with free or potentially free reducing groups, give an orangeyellow color w-hen treated with phenol and concentrated sulfuric acid. The reaction is sensitive and the color is stable. By use of this phenol-sulfuric acid reaction, a method has been developed to determine submicro amounts of sugars and related substances. In conjunction with paper partition chromatography the method is useful for the determination of the composition of polysaccharides and their methyl derivatives.
Rényi Divergence and Kullback-Leibler Divergence

Rényi divergence is related to Rényi entropy much like Kullback-Leibler divergence is related to Shannon's entropy, and comes up in many settings. It was introduced by Rényi as a measure of information that satisfies almost the same axioms as Kullback-Leibler divergence, and depends on a parameter that is called its order. In particular, the Rényi divergence of order 1 equals the Kullback-Leibler divergence. We review and extend the most important properties of Rényi divergence and Kullback- Leibler divergence, including convexity, continuity, limits of σ-algebras, and the relation of the special order 0 to the Gaussian dichotomy and contiguity. We also show how to generalize the Pythagorean inequality to orders different from 1, and we extend the known equivalence between channel capacity and minimax redundancy to continuous channel inputs (for all orders) and present several other minimax results.
How bodies matter: five themes for interaction design

Our physical bodies play a central role in shaping human experience in the world, understandingof the world, and  interactions in the world. This paper draws on theories of embodiment - from psychology, sociology, and philosophy - synthesizing five themes we believe are particularly salient for interaction design: thinking through doing, performance, visibility, risk, and thick practice. We intro-duce aspects of human embodied engagement in the world with the goal of inspiring new interaction design ap-proaches and evaluations that better integrate the physical and computational worlds.
Entropic Brain-computer Interfaces - Using fNIRS and EEG to Measure Attentional States in a Bayesian Framework

Implicit Brain-Computer Interfaces (BCI) adapt system settings subtly based on real time measures of brain activation without the user’s explicit awareness. For example, measures of the user’s cognitive workload might drive a system that alters the timing of notifications in order to minimize user interruption. Here, we consider new avenues for implicit BCI based on recent discoveries in cognitive neuroscience and conduct a series of experiments using BCI’s principal non-invasive brain sensors, fNIRS and EEG. We show how Bayesian and systems neuroscience formulations explain the difference in performance of machine learning algorithms trained on brain data in different conditions. These new formulations posit that the brain aims to minimize its long-term surprisal of sensory data and organizes its calculations on two anti-correlated networks. We consider how to use real-time input that portrays a user along these dimensions in designing Bidirectional BCIs, which are Implicit BCIs that aim to optimize the user’s state by modulating computer output based on feedback from a brain monitor. We introduce Entropic Brain-Computer Interfacing as a type of Bidirectional BCI which uses physiological measurements of information theoretical dimensions of the user’s state to evaluate the digital flow of information to the user’s brain, tweaking this output in a feedback loop to the user’s benefit.
Machine Learning for Performance Prediction in Mobile Cellular Networks

In this paper, we discuss the application of machine learning techniques for performance prediction problems in wireless networks. These problems often involve using existing measurement data to predict network performance where direct measurements are not available. We explore the performance of existing machine learning algorithms for these problems and propose a simple taxonomy of main problem categories. As an example, we use an extensive real-world drive test data set to show that classical machine learning methods such as Gaussian process regression, exponential smoothing of time series, and random forests can yield excellent prediction results. Applying these methods to the management of wireless mobile networks has the potential to significantly reduce operational costs while simultaneously improving user experience. We also discuss key challenges for future work, especially with the focus on practical deployment of machine learning techniques for performance prediction in mobile wireless networks.
Wan2tlk?: everyday text messaging

Texting--using a mobile phone to send text messages--has become a form of mass communication. Building on studies that described how British teenagers have incorporated text messaging into their lives, we examine the purposes and nature of the conversations themselves. We also present findings that suggest that teenagers do not have many simultaneous multiple conversations via text messaging; end most text messaging conversations by switching to another medium; and, that, despite popular beliefs, communicate with surprisingly few friends via their mobile phones. Finally we describe how and what words they shorten in their text messages.
Object detection by spatio-temporal analysis and tracking of the detected objects in a video with variable background

In this paper we propose a novel approach for detecting and tracking objects in videos with variable background i.e. videos captured by moving cameras without any additional sensor. The performance of tracking in videos with variable background depends on the successful detection of an object in variable background. The most attractive feature of detecting an object in variable background is that it does not depend on any a priori information of the scene. In a video captured by a moving camera, both the background and foreground are changing in each frame of the image sequence. So for these videos, modeling a single background with traditional background modeling methods is infeasible and thus the detection of actual moving object in a variable background is a challenging task. To detect actual moving object in this work, spatio-temporal blobs have been generated in each frame by spatio-temporal analysis of the image sequence using a three-dimensional Gabor filter. Then individual blobs, which are parts of one object are merged using Minimum Spanning Tree to form the moving object in the variable background. The height, width and four-bin grayvalue histogram of the object are calculated as its features and an object is tracked in each frame using these features to generate the trajectories of the object through the video sequence. In this work, problem of data association during tracking is solved by Linear Assignment Problem and occlusion is handled by the application of kalman filter. The major advantage of our method over most of the existing tracking algorithms is that, the proposed method does not require initialization in the first frame or training on sample data to perform. Performance of the algorithm has been tested on benchmark videos and very satisfactory result has been achieved. The performance of the algorithm is also comparable and superior with respect to some benchmark algorithms.