nils-herrmann/clean_scirepeval huggingface dataset:
A classification of web browser fingerprinting techniques

As the World Wide Web has increasingly become a necessity in daily life, the acute need to safeguard user privacy and security has become manifestly apparent. After users realized that browser cookies could allow websites to track their actions without permission or notification, many have chosen to reject cookies in order to protect their privacy. However, more recently, methods of fingerprinting a web browser have become an increasingly common practice. In this paper, we classify web browser fingerprinting into four main categories: (1) Browser Specific, (2) Canvas, (3) JavaScript Engine, and (4) Cross-browser. We then summarize the privacy and security implications, discuss commercial fingerprinting techniques, and finally present some detection and prevention methods.
A Very Rapid Prediction of IM Performance Combining Analytical and Finite-Element Analysis

A rapid and accurate analysis of the induction motor (IM) is the basis of any design process. This paper deals with an analysis procedure of the IM that combines a set of finite-element simulations with an analytical model. Both three- and single-phase machines are considered. Experimental tests on some IMs confirm the accuracy of the prediction achieved by the proposed analysis. The choice of specific quantities releases the results from the actual motor length and extends them to any motor adopting the same lamination and winding distribution.
Automated person recognition by walking and running via model-based approaches

Gait enjoys advantages over other biometrics in that it can be perceived from a distance and is di/cult to disguise. Current approaches are mostly statistical and concentrate on walking only. By analysing leg motion we show how we can recognise people not only by the walking gait, but also by the running gait. This is achieved by either of two new modelling approaches which employ coupled oscillators and the biomechanics of human locomotion as the underlying concepts. These models give a plausible method for data reduction by providing estimates of the inclination of the thigh and of the leg, from the image data. Both approaches derive a phase-weighted Fourier description gait signature by automated non-invasive means. One approach is completely automated whereas the other requires speci5cation of a single parameter to distinguish between walking and running. Results show that both gaits are potential biometrics, with running being more potent. By its basis in evidence gathering, this new technique can tolerate noise and low resolution. ? 2003 Pattern Recognition Society.
Using no-show modeling to improve clinic performance

'No-shows' or missed appointments result in under-utilized clinic capacity. We develop a logistic regression model using electronic medical records to estimate patients' no-show probabilities and illustrate the use of the estimates in creating clinic schedules that maximize clinic capacity utilization while maintaining small patient waiting times and clinic overtime costs. This study used information on scheduled outpatient appointments collected over a three-year period at a Veterans Affairs medical center. The call-in process for 400 clinic days was simulated and for each day two schedules were created: the traditional method that assigned one patient per appointment slot, and the proposed method that scheduled patients according to their no-show probability to balance patient waiting, overtime and revenue. Combining patient no-show models with advanced scheduling methods would allow more patients to be seen a day while improving clinic efficiency. Clinics should consider the benefits of implementing scheduling software that includes these methods relative to the cost of no-shows.
A Single-Stage PV Module Integrated Converter Based on a Low-Power Current-Source Inverter

This paper presents a transformerless three-phase inverter designed for the integration into a special type of a photovoltaic (PV) module, which is capable of providing an output voltage of several hundred volts. The chosen topology, a current-source inverter, features a single-stage power conversion system that directly feeds into the grid. The principle operation and control is described, and a modified modulation strategy is proposed to attenuate common-mode currents. A robust and highly efficient laboratory prototype of a 250-W module integrated converter has been implemented and tested. Its compact and flat design allows the direct attachment to the PV module.
Image Recognition of 85 Food Categories by Feature Fusion

Recognition of food images is challenging due to their diversity and practical for health care on foods for people. In this paper, we propose an automatic food image recognition system for 85 food categories by fusing various kinds of image features including bag-of-features~(BoF), color histogram, Gabor features and gradient histogram with Multiple Kernel Learning~(MKL). In addition, we implemented a prototype system to recognize food images taken by cellular-phone cameras. In the experiment, we have achieved the 62.52% classification rate for 85 food categories.
Deep Learning and Convolutional Neural Networks for Medical Image Computing

These are exciting times for medical image processing. Innovations in deep learning and the increasing availability of large annotated medical image datasets are leading to dramatic advances in automated understanding of medical images. From this perspective, I give a personal view of how computer-aided diagnosis of medical images has evolved and how the latest advances are leading to dramatic improvements today. I discuss the impact of deep learning on automated disease detection and organ and lesion segmentation, with particular attention to applications in diagnostic radiology. I provide some examples of how time-intensive and expensive manual annotation of huge medical image datasets by experts can be sidestepped by using weakly supervised learning from routine clinically generated medical reports. Finally, I identify the remaining knowledge gaps that must be overcome to achieve clinician-level performance of automated medical image processing systems. Computer-aided diagnosis (CAD) in medical imaging has flourished over the past several decades. New advances in computer software and hardware and improved quality of images from scanners have enabled this progress. The main motivations for CAD have been to reduce error and to enable more efficient measurement and interpretation of images. From this perspective, I will describe how deep learning has led to radical changes in howCAD research is conducted and in howwell it performs. For brevity, I will include automated disease detection and image processing under the rubric of CAD. Financial Disclosure The author receives patent royalties from iCAD Medical. Disclaimer No NIH endorsement of any product or company mentioned in this manuscript should be inferred. The opinions expressed herein are the author’s and do not necessarily represent those of NIH. R.M. Summers (B) Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bldg. 10, Room 1C224D MSC 1182, Bethesda, MD 20892-1182, USA e-mail: rms@nih.gov URL: http://www.cc.nih.gov/about/SeniorStaff/ronald_summers.html © Springer International Publishing Switzerland 2017 L. Lu et al. (eds.), Deep Learning and Convolutional Neural Networks for Medical Image Computing, Advances in Computer Vision and Pattern Recognition, DOI 10.1007/978-3-319-42999-1_1 3
SUSTAINABLE COMPETITIVE ADVANTAGE : COMBINING INSTITUTIONAL AND RESOURCE-BASED VIEWS

This article suggests that the context and process of resource selection have an important influence on firm heterogeneity and sustainable competitive advantage. It is argued that a firm’s sustainable advantage depends on its ability to manage the institutional context of its resource decisions. A firm’s institutional context includes its internal culture as well as broader influences from the state, society, and interfirm relations that define socially acceptable economic behavior. A process model of firm heterogeneity is proposed that combines the insights of a resourcebased view with the institutional perspective from organization theory. Normative rationality, institutional isolating mechanisms, and institutional sources of firm homogeneity are proposed as determinants of rent potential that complement and extend resource-based explanations of firm variation and sustainable competitive advantage. The article suggests that both resource capital and institutional capital are indispensable to sustainable competitive advantage.
One-Shot Relational Learning for Knowledge Graphs

Knowledge graphs (KGs) are the key components of various natural language processing applications. To further expand KGs’ coverage, previous studies on knowledge graph completion usually require a large number of training instances for each relation. However, we observe that long-tail relations are actually more common in KGs and those newly added relations often do not have many known triples for training. In this work, we aim at predicting new facts under a challenging setting where only one training instance is available. We propose a one-shot relational learning framework, which utilizes the knowledge extracted by embedding models and learns a matching metric by considering both the learned embeddings and one-hop graph structures. Empirically, our model yields considerable performance improvements over existing embedding models, and also eliminates the need of retraining the embedding models when dealing with newly added
Delta robot : inverse , direct , and intermediate Jacobians

In the context of a parallel manipulator, inverse and direct Jacobian matrices are known to contain information which helps us identify some of the singular configurations. In this article, we employ kinematic analysis for the Delta robot to derive the velocity of the end-effector in terms of the angular joint velocities, thus yielding the Jacobian matrices. Setting their determinants to zero, several undesirable postures of themanipulator have been extracted. The analysis of the inverse Jacobian matrix reveals that singularities are encountered when the limbs belonging to the same kinematic chain lie in a plane. Two of the possible configurations which correspond to this condition are when the robot is completely extended or contracted, indicating the boundaries of the workspace. Singularities associated with the direct Jacobian matrix, which correspond to relatively more complicated configurations of the manipulator, have also been derived and commented on. Moreover, the idea of intermediate Jacobian matrices have been introduced that are simpler to evaluate but still contain the information of the singularities mentioned earlier in addition to architectural singularities not contemplated in conventional Jacobians.
Regulation of Photovoltaic Voltage

In photovoltaic power systems, both photovoltaic modules and switching-mode converters present nonlinear and time-variant characteristics, which result in a difficult control problem. This paper presents an in-depth analysis and modeling to discover the inherent features of a photovoltaic power system. The method of successive linearization simplifies the nonlinear problem back to the linear case. This paper also presents the use of Youla parameterization to design a stable control system for regulating the photovoltaic voltage. The experimental and simulation results demonstrate the effectiveness of the presented analysis, design, and implementation.
Efficient hybrid solar cells based on meso-superstructured organometal halide perovskites.

The energy costs associated with separating tightly bound excitons (photoinduced electron-hole pairs) and extracting free charges from highly disordered low-mobility networks represent fundamental losses for many low-cost photovoltaic technologies. We report a low-cost, solution-processable solar cell, based on a highly crystalline perovskite absorber with intense visible to near-infrared absorptivity, that has a power conversion efficiency of 10.9% in a single-junction device under simulated full sunlight. This "meso-superstructured solar cell" exhibits exceptionally few fundamental energy losses; it can generate open-circuit photovoltages of more than 1.1 volts, despite the relatively narrow absorber band gap of 1.55 electron volts. The functionality arises from the use of mesoporous alumina as an inert scaffold that structures the absorber and forces electrons to reside in and be transported through the perovskite.
TAM-based success modeling in ERP

The literature assumes that Enterprise Resource Planning (ERP) systems are complex tools. Due to this complexity, ERP produce negative impacts on the users’ acceptation. However, few studies have tried to identify the factors that influence the ERP users’ acceptance. This paper’s aim is to focus on decisive factors influencing the ERP users’ acceptance and use. Specifically, the authors have developed a research model based on the Technology Acceptance Model (TAM) for testing the influence of the Critical Success Factors (CSFs) on ERP implementation. The CSFs used are: (1) top management support, (2) communication, (3) cooperation, (4) training and (5) technological complexity. This research model has offered some evidence about main acceptance factors on ERP which help to set the users’ behavior toward ERP.
Financial time series forecasting using support vector machines

Support vector machines (SVMs) are promising methods for the prediction of -nancial timeseries because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. This study applies SVM to predicting the stock price index. In addition, this study examines the feasibility of applying SVM in -nancial forecasting by comparing it with back-propagation neural networks and case-based reasoning. The experimental results show that SVM provides a promising alternative to stock market prediction.
Deep Learning-Based Fast Hand Gesture Recognition Using Representative Frames

In this paper, we propose a vision-based hand gesture recognition system for intelligent vehicles. Vision-based gesture recognition systems are employed in automotive user interfaces to increase the driver comfort without compromising their safety. In our algorithm, the long-term recurrent convolution network is used to classify the video sequences of hand gestures. In the standard long-term recurrent convolution network-based action classifier, multiple frames sampled from the video sequence are given as an input to the network, to perform classification. However, the use of multiple frames increases the computational complexity, apart from reducing the classification accuracy of the classifier. We propose to address these issues by extracting a fewer representative frames from the video sequence, and inputting them to the long-term recurrent convolution network. To extract the representative frames, we propose to use novel tiled image patterns and tiled binary pattern within a semantic segmentation- based deep learning framework, the deconvolutional neural network. The novel tiled image patterns contain multiple non-overlapping blocks and represent the entire gesture-based video sequence within a single tiled image. These image patterns represent the input to the deconvolution network and are generated from the video sequence. The novel tiled binary pattern also contain multiple non-overlapping blocks and represent the representative frames of the video sequence. These binary patterns represent the output of the deconvolution network. The training binary patterns are generated from the training video sequences using the dictionary learning and sparse modeling framework. We validate our proposed algorithm on the public Cambridge gesture recognition dataset. A comparative analysis is performed with baseline algorithms and an improved classification accuracy is observed. We also perform a detailed parametric analysis of the proposed algorithm. We report a gesture classification accuracy of 91% and report a near real-time computational complexity of $110$~ms per video sequence.
The fear of being laughed at : Individual and group differences in Gelotophobia *

Single case studies led to the discovery and phenomenological description of Gelotophobia and its definition as the pathological fear of appearing to social partners as a ridiculous object (Titze 1995, 1996, 1997). The aim of the present study is to empirically examine the core assumptions about the fear of being laughed at in a sample comprising a total of 863 clinical and non-clinical participants. Discriminant function analysis yielded that gelotophobes can be separated from other shame-based neurotics, non-shamebased neurotics, and controls. Separation was best for statements specifically describing the gelotophobic symptomatology and less potent for more general questions describing socially avoidant behaviors. Factor analysis demonstrates that while Gelotophobia is composed of a set of correlated elements in homogenous samples, overall the concept is best conceptualized as unidimensional. Predicted and actual group membership converged well in a cross-classification (approximately 69% of correctly classified cases). Overall, it can be concluded that the fear of being laughed at varies tremendously among adults and might hold a key to understanding certain forms
A Miniaturized Antenna with Negative Index Metamaterial Based on Modified SRR and CLS Unit Cell for UWB Microwave Imaging Applications

A miniaturized antenna employing a negative index metamaterial with modified split-ring resonator (SRR) and capacitance-loaded strip (CLS) unit cells is presented for Ultra wideband (UWB) microwave imaging applications. Four left-handed (LH) metamaterial (MTM) unit cells are located along one axis of the antenna as the radiating element. Each left-handed metamaterial unit cell combines a modified split-ring resonator (SRR) with a capacitance-loaded strip (CLS) to obtain a design architecture that simultaneously exhibits both negative permittivity and negative permeability, which ensures a stable negative refractive index to improve the antenna performance for microwave imaging. The antenna structure, with dimension of 16 × 21 × 1.6 mm³, is printed on a low dielectric FR4 material with a slotted ground plane and a microstrip feed. The measured reflection coefficient demonstrates that this antenna attains 114.5% bandwidth covering the frequency band of 3.4-12.5 GHz for a voltage standing wave ratio of less than 2 with a maximum gain of 5.16 dBi at 10.15 GHz. There is a stable harmony between the simulated and measured results that indicate improved nearly omni-directional radiation characteristics within the operational frequency band. The stable surface current distribution, negative refractive index characteristic, considerable gain and radiation properties make this proposed negative index metamaterial antenna optimal for UWB microwave imaging applications.
Accelerating vector graphics rendering using the graphics hardware pipeline

We describe our successful initiative to accelerate Adobe Illustrator with the graphics hardware pipeline of modern GPUs. Relying on OpenGL 4.4 plus recent OpenGL extensions for advanced blend modes and first-class GPU-accelerated path rendering, we accelerate the Adobe Graphics Model (AGM) layer responsible for rendering sophisticated Illustrator scenes. Illustrator documents render in either an RGB or CMYK color mode. While GPUs are designed and optimized for RGB rendering, we orchestrate OpenGL rendering of vector content in the proper CMYK color space and accommodate the 5+ color components required. We support both non-isolated and isolated transparency groups, knockout, patterns, and arbitrary path clipping. We harness GPU tessellation to shade paths smoothly with gradient meshes. We do all this and render complex Illustrator scenes 2 to 6x faster than CPU rendering at Full HD resolutions; and 5 to 16x faster at Ultra HD resolutions.
Improving Curated Web-Data Quality with Structured Harvesting and Assessment

This paper describes a semi-automated process, framework and tools for harvesting, assessing, improving and maintaining high-quality linked-data. The framework, known as DaCura1, provides dataset curators, who may not be knowledge engineers, with tools to collect and curate evolving linked data datasets that maintain quality over time. The framework encompasses a novel process, workflow and architecture. A working implementation has been produced and applied firstly to the publication of an existing social-sciences dataset, then to the harvesting and curation of a related dataset from an unstructured data-source. The framework’s performance is evaluated using data quality measures that have been developed to measure existing published datasets. An analysis of the framework against these dimensions demonstrates that it addresses a broad range of real-world data quality concerns. Experimental results quantify the impact of the DaCura process and tools on data quality through an assessment framework and methodology which combines automated and human data quality controls. Improving Curated WebData Quality with Structured Harvesting and Assessment
From subtitles to human interactions : introducing the SubTle Corpus

Domain-oriented dialogue systems are often faced with users that try to cross the limits of their knowledge, by unawareness of its domain limitations or simply to test their capacity. These interactions are considered to be Out-Of-Domain and several strategies can be found in the literature to deal with some specific situations. Since if a certain input appears once, it has a non-zero probability of being entered later, the idea of taking advantage of real human interactions to feed these dialogue systems emerges, thus, naturally. In this paper, we introduce the SubTle Corpus, a corpus of Interaction-Response pairs extracted from subtitles files, created to help dialogue systems to deal with Out-of-Domain interactions.
A text reading algorithm for natural images

Reading text in natural images has focused again the attention of many researchers during the last few years due to the increasingly availability of cheap image-capturing devices in low-cost products like mobile phones. Therefore, as text can be found on any environment, the applicability of text-reading systems is really extensive. For this purpose, we present in this paper a robust method to read text in natural images. It is composed of two main separated stages. Firstly, text is located in the image using a set of simple and fast-tocompute features highly discriminative between character and non-character objects. They are based on geometric and gradient properties. The second part of the system carries out the recognition of the previously detected text. It uses gradient features to recognize single characters and Dynamic Programming (DP) to correct misspelled words. Experimental results obtained with different challenging datasets show that the proposed system exceeds state-of-the-art performance, both in terms of localization and recognition.
Real-time lane detection in various conditions and night cases

In this paper, we propose a real-time lane detection algorithm based on a hyperbola-pair lane boundary model and an improved RANSAC paradigm. Instead of modeling each road boundary separately, we propose a model to describe the road boundary as a pair of parallel hyperbolas on the ground plane. A fuzzy measurement is introduced into the RANSAC paradigm to improve the accuracy and robustness of fitting the points on the boundaries into the model. Our method is able to deal with existence of partial occlusion, other traffic participants and markings, etc. Experiment in many different conditions, including various conditions of illumination, weather and road, demonstrates its high performance and accuracy