nils-herrmann/clean_scirepeval huggingface dataset:
Parking availability prediction for sensor-enabled car parks in smart cities

The growth in low-cost, low-power sensing and communication technologies is creating a pervasive network infrastructure called the Internet of Things (IoT), which enables a wide range of physical objects and environments to be monitored in fine spatial and temporal detail. The detailed, dynamic data that can be collected from these devices provide the basis for new business and government applications in areas such as public safety, transport logistics and environmental management. There has been growing interest in the IoT for realising smart cities, in order to maximise the productivity and reliability of urban infrastructure, such as minimising road congestion and making better use of the limited car parking facilities. In this work, we consider two smart car parking scenarios based on real-time car parking information that has been collected and disseminated by the City of San Francisco, USA and the City of Melbourne, Australia. We present a prediction mechanism for the parking occupancy rate using three feature sets with selected parameters to illustrate the utility of these features. Furthermore, we analyse the relative strengths of different machine learning methods in using these features for prediction.
Does multitasking with mobile phones affect learning? A review

Mobile phone multitasking is widely considered to be a major source of distraction in academic performance. This paper attempts to review the emerging literature by focusing on three questions concerning the influence of mobile phone multitasking on academic performance: (a) How does mobile phone multitasking impair learning? (b) Why does mobile phone use impair learning? (c) How to prevent from mobile phone distraction? We use multiple strategies to locate the existing research literature and identified 132 studies published during 1999e2014. The mobile phone multitasking and distractibility are reviewed in three major aspects: distraction sources (ring of mobile phone, texting, and social application), distraction targets (reading and attending), and distraction subjects (personality, gender, and culture). We also compare the results of these studies with the findings on mobile phone multitasking and driving, the earliest area of mobile phone multitasking research. Both limitations of existing research and future research directions are discussed.
A surgical protocol for bicondylar four-quadrant tibial plateau fractures

Bicondylar tibial plateau fractures involving four articular quadrants are severe and complex injuries, and they remain a challenging problem in orthopaedic trauma. The aim of this study was to introduce a new treatment protocol with dual-incision and multi-plate fixation in the floating supine patient position as well as to report the preliminary clinical results. From January 2006 to December 2011, 16 consecutive patients with closed bicondylar four-quadrant tibial plateau fractures (Schatzker type VI, OTA/AO 41C2/3) were treated with posteromedial inverted L-shaped and anterolateral incisions. With the posteromedial approach, three quadrants (posteromedial, anteromedial and posterolateral) can be exposed, reduced and fixed with multiple small antiglide plates and short screws in an enclosure pattern. With the anterolateral approach, after articular elevation and bone substitute grafting, a strong locking plate with long screws to the medial cortex is used to raft-buttress the reduced lateral plateau fracture, hold the entire reconstructed tibial condyles together, and contact the condyles with the tibial shaft. All patients were encouraged to exercise knee motion at an early stage. The outcome was evaluated clinically and radiologically after a minimum two-year follow-up. The average operation time was 98 ± 26 minutes (range 70–128) and the average duration of hospitalization was 29 ± 8.6 days (range 20–41). Three cases used five plates, nine cases used four plates, and four cases used three plates. All patients were followed for a mean of 28.7 ± 6.1 months (range 26–38). Fifteen incisions healed initially, while one patient developed a medial wound dehiscence and was successfully managed by debridement. All patients achieved radiological fracture union after an average of 20.2 weeks. At the two-year follow up, the average knee range of motion (ROM) was 98° ± 13.7 (range 88–125°), with a Hospital for Special Surgery (HSS) knee score of 87.7 ± 10.3 (range 75–95), and SMFA score of 21.3 ± 8.6 (range 12–33). For bicondylar four-quadrant tibial plateau fractures, the treatment protocol of multiple medial-posterior small plates combined with a lateral strong locking plate through dual incisions can provide stable fracture fixation to allow for early stage rehabilitation. Good clinical outcomes can be anticipated.
HoloDesk: direct 3d interactions with a situated see-through display

HoloDesk is an interactive system combining an optical see through display and Kinect camera to create the illusion that users are directly interacting with 3D graphics. A virtual image of a 3D scene is rendered through a half silvered mirror and spatially aligned with the real-world for the viewer. Users easily reach into an interaction volume displaying the virtual image. This allows the user to literally get their hands into the virtual display and to directly interact with an spatially aligned 3D virtual world, without the need for any specialized head-worn hardware or input device. We introduce a new technique for interpreting raw Kinect data to approximate and track rigid (e.g., books, cups) and non-rigid (e.g., hands, paper) physical objects and support a variety of physics-inspired interactions between virtual and real. In particular the algorithm models natural human grasping of virtual objects with more fidelity than previously demonstrated. A qualitative study highlights rich emergent 3D interactions, using hands and real-world objects. The implementation of HoloDesk is described in full, and example application scenarios explored. Finally, HoloDesk is quantitatively evaluated in a 3D target acquisition task, comparing the system with indirect and glasses-based variants.
Enhancing security and privacy in biometrics-based authentication systems

Because biometrics-based authentication offers several advantages over other authentication methods, there has been a significant surge in the use of biometrics for user authentication in recent years. It is important that such biometrics-based authentication systems be designed to withstand attacks when employed in security-critical applications, especially in unattended remote applications such as ecommerce. In this paper we outline the inherent strengths of biometrics-based authentication, identify the weak links in systems employing biometrics-based authentication, and present new solutions for eliminating some of these weak links. Although, for illustration purposes, fingerprint authentication is used throughout, our analysis extends to other biometrics-based methods.
A Circularly-Polarized Isoflux Antenna Based on Anisotropic Metasurface

Theory, design, realization and measurements of an X-band isoflux circularly polarized antenna for LEO satellite platforms are presented. The antenna is based on a metasurface composed by a dense texture of sub-wavelength metal patches on a grounded dielectric slab, excited by a surface wave generated by a coplanar feeder. The antenna is extremely flat (1.57 mm) and light (less than 1 Kg) and represents a competitive solution for space-to-ground data link applications.
Delivering on the promise of universal memory for spin-transfer torque RAM (STT-RAM)

Spin-Transfer Torque RAM (STT-RAM) has emerged as a potential candidate for Universal memory. However, there are two challenges to using STT-RAM in memory system design: (1) the intrinsic variation in the storage element, the Magnetic Tunnel Junction (MTJ), and (2) the high write energy. In this paper, we present a physically based thermal noise model for simulating the statistical variations of MTJs. We have implemented it in HSPICE and validated it against analytical results. We demonstrate its use in setting the write pulse width for a given write error rate. We then propose two write-energy reduction techniques. At the device level, we propose the use of a low-MS ferromagnetic material that can reduce the write energy without sacrificing retention time. At the architecture level, we show that Invert Coding provides a 7% average reduction in the total write energy for the SPEC CPU2006 benchmark suite without any performance overhead.
Strategic Planning with Critical Success Factors and Future Scenarios : An Integrated Strategic Planning Framework

The ideas and findings in this report should not be construed as an official DoD position. It is published in the interest of scientific and technical information exchange. Use of any trademarks in this report is not intended in any way to infringe on the rights of the trademark holder. Internal use. Permission to reproduce this document and to prepare derivative works from this document for internal use is granted, provided the copyright and " No Warranty " statements are included with all reproductions and derivative works. External use. This document may be reproduced in its entirety, without modification, and freely distributed in written or electronic form without requesting formal permission. Permission is required for any other external and/or commercial use. a federally funded research and development center. The Government of the United States has a royalty-free government-purpose license to use, duplicate, or disclose the work, in whole or in part and in any manner, and to have or permit others to do so, for government purposes pursuant to the copyright license under the clause at 252.227-7013.  Structure of this Report 1 1.2 Background 1 1.3 The Strategic Planning Landscape 1
Semantic (Web) Technology In Action: Ontology Driven Information Systems for Search, Integration and Analysis

Semantics is seen as the key ingredient in the next phase of the Web infrastructure as well as the next generation of information systems applications. In this context, we review some of the reservations expressed about the viability of the Semantic Web. We respond to these by identifying a Semantic Technology that supports the key capabilities also needed to realize the Semantic Web vision, namely representing, acquiring and utilizing knowledge. Given that scalability is a key challenge, we briefly review our observations from developing three classes of real world applications and corresponding technology components: search/browsing, integration, and analytics. We distinguish this proven technology from some parts of the Semantic Web approach and offer subjective remarks which we hope will foster additional debate.
Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification

Person Re-identification (ReID) is to identify the same person across different cameras. It is a challenging task due to the large variations in person pose, occlusion, background clutter, etc. How to extract powerful features is a fundamental problem in ReID and is still an open problem today. In this paper, we design a Multi-Scale Context-Aware Network (MSCAN) to learn powerful features over full body and body parts, which can well capture the local context knowledge by stacking multi-scale convolutions in each layer. Moreover, instead of using predefined rigid parts, we propose to learn and localize deformable pedestrian parts using Spatial Transformer Networks (STN) with novel spatial constraints. The learned body parts can release some difficulties, e.g. pose variations and background clutters, in part-based representation. Finally, we integrate the representation learning processes of full body and body parts into a unified framework for person ReID through multi-class person identification tasks. Extensive evaluations on current challenging large-scale person ReID datasets, including the image-based Market1501, CUHK03 and sequence-based MARS datasets, show that the proposed method achieves the state-of-the-art results.
A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition

Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR) problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study. Therefore, in this paper, we first create the most complete dataset, focusing on accelerometer sensors, with various sources of heterogeneities. We then conduct an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers) for activity recognition. Principal component analysis is applied to reduce the feature vector dimension while keeping essential information. The average classification accuracy of eight sensor positions is reported to be 96.44% ± 1.62% with 10-fold evaluation, whereas accuracy of 79.92% ± 9.68% is reached in the subject-independent evaluation. This study presents significant evidence that we can build predictive models for HAR problem under more realistic conditions, and still achieve highly accurate results.
Individualization of Information Systems - Analyzing Antecedents of IT Consumerization Behavior

Recently, much attention in research and practice has been devoted to the topic of IT consumerization, referring to the adoption of private consumer IT in the workplace. However, research lacks an analysis of possible antecedents of the trend on an individual level. To close this gap, we derive a theoretical model for IT consumerization behavior based on the theory of planned behavior and perform a quantitative analysis. Our investigation shows that it is foremost determined by normative pressures, specifically the behavior of friends, co-workers and direct supervisors. In addition, behavioral beliefs and control beliefs were found to affect the intention to use non-corporate IT. With respect to the former, we found expected performance improvements and an increase in ease of use to be two of the key determinants. As for the latter, especially monetary costs and installation knowledge were correlated with IT consumerization intention.
Face lift.

After reading this article, the participant should be able to: 1. Identify and describe the anatomy of and changes to the aging face, including changes in bone mass and structure and changes to the skin, tissue, and muscles. 2. Assess each individual's unique anatomy before embarking on face-lift surgery and incorporate various surgical techniques, including fat grafting and other corrective procedures in addition to shifting existing fat to a higher position on the face, into discussions with patients. 3. Identify risk factors and potential complications in prospective patients. 4. Describe the benefits and risks of various techniques.



The ability to surgically rejuvenate the aging face has progressed in parallel with plastic surgeons' understanding of facial anatomy. In turn, a more clear explanation now exists for the visible changes seen in the aging face. This article and its associated video content review the current understanding of facial anatomy as it relates to facial aging. The standard face-lift techniques are explained and their various features, both good and bad, are reviewed. The objective is for surgeons to make a better aesthetic diagnosis before embarking on face-lift surgery, and to have the ability to use the appropriate technique depending on the clinical situation.
Material Recognition from Local Appearance in Global Context

Recognition of materials has proven to be a challenging problem due to the wide variation in appearance within and between categories. Global image context, such as where the material is or what object it makes up, can be crucial to recognizing the material. Existing methods, however, operate on an implicit fusion of materials and context by using large receptive fields as input (i.e., large image patches). Many recent material recognition methods treat materials as yet another set of labels like objects. Materials are, however, fundamentally different from objects as they have no inherent shape or defined spatial extent. Approaches that ignore this can only take advantage of limited implicit context as it appears during training. We instead show that recognizing materials purely from their local appearance and integrating separately recognized global contextual cues including objects and places leads to superior dense, per-pixel, material recognition. We achieve this by training a fully-convolutional material recognition network end-toend with only material category supervision. We integrate object and place estimates to this network from independent CNNs. This approach avoids the necessity of preparing an impractically-large amount of training data to cover the product space of materials, objects, and scenes, while fully leveraging contextual cues for dense material recognition. Furthermore, we perform a detailed analysis of the effects of context granularity, spatial resolution, and the network level at which we introduce context. On a recently introduced comprehensive and diverse material database [14], we confirm that our method achieves state-of-the-art accuracy with significantly less training data compared to past methods.
Locating Facial Features with an Extended Active Shape Model

We make some simple extensions to the Active Shape Model of Cootes et al. , and use it to locate features in frontal views of upright faces. We show on independent test data that with the extensions the Active Shape Model compares favorably with more sophisticated methods. The extensions are (i) fitting more landmarks than are actually needed (ii) selectively using twoinstead of one-dimensional landmark templates (iii) adding noise to the training set (iv) relaxing the shape model where advantageous (v) trimming covariance matrices by setting most entries to zero, and (vi) stacking two Active Shape Models in series.
Finite-Approximation-Error-Based Optimal Control Approach for Discrete-Time Nonlinear Systems

In this paper, a new iterative adaptive dynamic programming (ADP) algorithm is developed to solve optimal control problems for infinite-horizon discrete-time nonlinear systems with finite approximation errors. The idea is to use an iterative ADP algorithm to obtain the iterative control law that makes the iterative performance index function reach the optimum. When the iterative control law and the iterative performance index function in each iteration cannot be accurately obtained, the convergence conditions of the iterative ADP algorithm are obtained. When convergence conditions are satisfied, it is shown that the iterative performance index functions can converge to a finite neighborhood of the greatest lower bound of all performance index functions under some mild assumptions. Neural networks are used to approximate the performance index function and compute the optimal control policy, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.
Recurrent Neural Network Postfilters for Statistical Parametric Speech Synthesis

In the last two years, there have been numerous papers that have looked into using Deep Neural Networks to replace the acoustic model in traditional statistical parametric speech synthesis. However, far less attention has been paid to approaches like DNN-based postfiltering where DNNs work in conjunction with traditional acoustic models. In this paper, we investigate the use of Recurrent Neural Networks as a potential postfilter for synthesis. We explore the possibility of replacing existing postfilters, as well as highlight the ease with which arbitrary new features can be added as input to the postfilter. We also tried a novel approach of jointly training the Classification And Regression Tree and the postfilter, rather than the traditional approach of training them independently.
Meshless Approximation Methods and Applications in Physics Based Modeling and Animation

With growing computing power, physical simulations have become increasingly important in computer graphics. Content creation for movies and interactive computer games relies heavily on physical models, and physicallyinspired interactions have proven to be a great metaphor for shape modeling. This tutorial will acquaint the reader with meshless methods for simulation and modeling. These methods differ from the more common grid or mesh-based methods in that they require less constraints on the spatial discretization. Since the algorithmic structure of simulation algorithms so critically depends on the underlying discretization, we will first treat methods for function approximation from discrete, irregular samples: smoothed particle hydrodynamics and moving least squares. This discussion will include numerical properties as well as complexity considerations. In the second part of this tutorial, we will then treat a number of applications for these approximation schemes. The smoothed particle hydrodynamics framework is used in fluid dynamics and has proven particularly popular in real-time applications. Moving least squares approximations provide higher order consistency, and are therefore suited for the simulation of elastic solids. We will cover both basic elasticity and applications in modeling.
Choosing DBSCAN Parameters Automatically using Differential Evolution

Over the last several years, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) has been widely applied in many areas of science due to its simplicity, robustness against noise (outlier) and ability to discover clusters of arbitrary shapes. However, DBSCAN algorithm requires two initial input parameters, namely Eps (the radius of the cluster) and MinPts (the minimum data objects required inside the cluster) which both have a significant influence on the clustering results. Hence, DBSCAN is sensitive to its input parameters and it is hard to determine them a priori. This paper presents an efficient and effective hybrid clustering method, named BDE-DBSCAN, that combines Binary Differential Evolution and DBSCAN algorithm to simultaneously quickly and automatically specify appropriate parameter values for Eps and MinPts. Since the Eps parameter can largely degrades the efficiency of the DBSCAN algorithm, the combination of an analytical way for estimating Eps and Tournament Selection (TS) method is also employed. Experimental results indicate the proposed method is precise in determining appropriate input parameters of DBSCAN algorithm.
Sliding-mode controller for four-wheel-steering vehicle: Trajectory-tracking problem

A solution to trajectory-tracking control problem for a four-wheel-steering vehicle (4WS) is proposed using sliding-mode approach. The advantage of this controller over current control procedure is that it is applicable to a large class of vehicles with single or double steering and to a tracking velocity that is not necessarily constant. The sliding-mode approach make the solutions robust with respect to errors and disturbances, as demonstrated by the simulation results.