nils-herrmann/clean_scirepeval huggingface dataset:
Celebrity Endorsement : How It Works When a Celebrity Fits the Brand and Advertisement

Celebrities are admired, appreciated and imitated all over the world. As a natural result of this, today many brands choose to work with celebrities for their advertisements. It can be said that the more the brands include celebrities in their marketing communication strategies, the tougher the competition in this field becomes and they allocate a large portion of their marketing budget to this. Brands invest in celebrities who will represent them in order to build the image they want to create. This study aimed to bring under spotlight the perceptions of Turkish customers regarding the use of celebrities in advertisements and marketing communication and try to understand their possible effects on subsequent purchasing decisions. In addition, consumers’ reactions and perceptions were investigated in the context of the product-celebrity match, to what extent the celebrity conforms to the concept of the advertisement and the celebrity-target audience match. In order to achieve this purpose, a quantitative research was conducted as a case study concerning Mavi Jeans (textile company). Information was obtained through survey. The results from this case study are supported by relevant theories concerning the main subject. The most valuable result would be that instead of creating an advertisement around a celebrity in demand at the time, using a celebrity that fits the concept of the advertisement and feeds the concept rather than replaces it, that is celebrity endorsement, will lead to more striking and positive results. Celebrity endorsement, product-celebrity match, advertising.
Musical expertise, bilingualism, and executive functioning.

The authors investigated whether intensive musical experience leads to enhancements in executive processing, as has been shown for bilingualism. Young adults who were bilinguals, musical performers (instrumentalists or vocalists), or neither completed 3 cognitive measures and 2 executive function tasks based on conflict. Both executive function tasks included control conditions that assessed performance in the absence of conflict. All participants performed equivalently for the cognitive measures and the control conditions of the executive function tasks, but performance diverged in the conflict conditions. In a version of the Simon task involving spatial conflict between a target cue and its position, bilinguals and musicians outperformed monolinguals, replicating earlier research with bilinguals. In a version of the Stroop task involving auditory and linguistic conflict between a word and its pitch, the musicians performed better than the other participants. Instrumentalists and vocalists did not differ on any measure. Results demonstrate that extended musical experience enhances executive control on a nonverbal spatial task, as previously shown for bilingualism, but also enhances control in a more specialized auditory task, although the effect of bilingualism did not extend to that domain.
Performance studies of a separately excited DC motor speed control fed by a buck converter using optimized PIλDμ controller

In this paper, fractional order proportional integral derivative controller (FOPID) has been designed to get a desired performance form a separately excited Direct Current (DC) motor fed by a buck converter. The robustness of the DC motor speed control is increased using this FOPID controller due to the effect of increased iso-damping response. This increased iso-damping occurs due to the addition of two new variables i.e. integro-differential operators λ and μ in FOPID when compared with conventional PID controller. We have also discussed the rule of taking this integro-differential operator which can be used for further improvement of iso-damping response. The five variables (Kp, Ki, Kd, λ and μ) of the FOPID controller are optimized first considering frequency domain design criteria and secondly using time domain performance index. Mathematical model of the plant has been used in order to utilize the present scheme. Genetic Algorithm from MATLAB optimization toolbox has been used for solving the time and frequency based design criteria.
Registration Techniques for Using Imperfect and Par tially Calibrated Devices in Planar Multi-Projector Displays

Multi-projector displays today are automatically registered, both geometrically and photometrically, using cameras. Existing registration techniques assume pre-calibrated projectors and cameras that are devoid of imperfections such as lens distortion. In practice, however, these devices are usually imperfect and uncalibrated. Registration of each of these devices is often more challenging than the multi-projector display registration itself. To make tiled projection-based displays accessible to a layman user we should allow the use of uncalibrated inexpensive devices that are prone to imperfections. In this paper, we make two important advances in this direction. First, we present a new geometric registration technique that can achieve geometric alignment in the presence of severe projector lens distortion using a relatively inexpensive low-resolution camera. This is achieved via a closed-form model that relates the projectors to cameras, in planar multi-projector displays, using rational Bezier patches. This enables us to geometrically calibrate a 3000 times 2500 resolution planar multi-projector display made of 3 times 3 array of nine severely distorted projectors using a low resolution (640 times 480) VGA camera. Second, we present a photometric self-calibration technique for a projector-camera pair. This allows us to photometrically calibrate the same display made of nine projectors using a photometrically uncalibrated camera. To the best of our knowledge, this is the first work that allows geometrically imperfect projectors and photometrically uncalibrated cameras in calibrating multi-projector displays.
Single-Feed Dual-Band Stacked Patch Antenna for Orthogonal Circularly Polarized GPS and SDARS Applications

We describe a single-feed stacked patch antenna design that is capable of simultaneously receiving both right hand circularly polarized (RHCP) satellite signals within the GPS LI frequency band and left hand circularly polarized (LHCP) satellite signals within the SDARS frequency band. In addition, the design provides improved SDARS vertical linear polarization (VLP) gain for terrestrial repeater signal reception at low elevation angles as compared to a current state of the art SDARS patch antenna.
Prosocial lies : When deception breeds trust

Philosophers, psychologists, and economists have long asserted that deception harms trust. We challenge this claim. Across four studies, we demonstrate that deception can increase trust. Specifically, prosocial lies increase the willingness to pass money in the trust game, a behavioral measure of benevolence-based trust. In Studies 1a and 1b, we find that altruistic lies increase trust when deception is directly experienced and when it is merely observed. In Study 2, we demonstrate that mutually beneficial lies also increase trust. In Study 3, we disentangle the effects of intentions and deception; intentions are far more important than deception for building benevolence-based trust. In Study 4, we examine how prosocial lies influence integrity-based trust. We introduce a new economic game, the Rely-or-Verify game, to measure integrity-based trust. Prosocial lies increase benevolence-based trust, but harm integrity-based trust. Our findings expand our understanding of deception and deepen our insight into the mechanics
A fuzzy logic based autonomous vehicle control system design in the TORCS environment

In this study, a fuzzy logic based autonomous vehicle control system is designed and tested in The Open Racing Car Simulator (TORCS) environment. The aim of this study is that vehicle complete the race without to get any damage and to get out of the way. In this context, an intelligent control system composed of fuzzy logic and conventional control structures has been developed such that the racing car is able to compete the race autonomously. In this proposed structure, once the vehicle's gearshifts have been automated, a fuzzy logic based throttle/brake control system has been designed such that the racing car is capable to accelerate/decelerate in a realistic manner as well as to drive at desired velocity. The steering control problem is also handled to end up with a racing car that is capable to travel on the road even in the presence of sharp curves. In this context, we have designed a fuzzy logic based positioning system that uses the knowledge of the curvature ahead to determine an appropriate position. The game performance of the developed fuzzy logic systems can be observed from https://youtu.be/qOvEz3-PzRo.
Camera Self-Calibration: Theory and Experiments

A b s t r a c t . The problem of finding the internal orientation of a camera (camera calibration) is extremely important for practical applications. In this paper a complete method for calibrating a camera is presented. In contrast with existing methods it does not require a calibration object with a known 3D shape. The new method requires only point matches from image sequences. It is shown, using experiments with noisy data, that it is possible to calibrate a camera just by pointing it at the environment, selecting points of interest and then tracking them in the image as the camera moves. It is not necessary to know the camera motion. The camera calibration is computed in two steps. In the first step the epipolar transformation is found. Two methods for obtaining the epipoles are discussed, one due to Sturm is based on projective invariants, the other is based on a generalisation of the essential matrix. The second step of the computation uses the so-called Kruppa equations which link the epipolar transformation to the image of the absolute conic. After the camera has made three or more movements the Kruppa equations can be solved for the coefficients of the image of the absolute conic. The solution is found using a continuation method which is briefly described. The intrinsic parameters of the camera are obtained from the equation for the image of the absolute conic. The results of experiments with synthetic noisy data are reported and possible enhancements to the method are suggested.
A 8-bit 500-KS/s low power SAR ADC for bio-medical applications

This paper presents a successive approximation register analog-to-digital converter (SAR ADC) design for bio-medical applications. An energy-saving switching sequence technique is proposed to achieve low power consumption. The average switching energy of the capacitor array can be reduced by 56% compared to a conventional switching method. The measured signal-to-noise-and-distortion ratios of the ADC is 46.92 dB at 500 KS/s sampling rate with an ultra-low power consumption of only 7.75-muW from a 1-V supply voltage. The ADC is fabricated in a 0.18-mum CMOS technology.
Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs

Chest X-ray is one of the most accessible medical imaging technique for diagnosis of multiple diseases. With the availability of ChestX-ray14, which is a massive dataset of chest X-ray images and provides annotations for 14 thoracic diseases; it is possible to train Deep Convolutional Neural Networks (DCNN) to build Computer Aided Diagnosis (CAD) systems. In this work, we experiment a set of deep learning models and present a cascaded deep neural network that can diagnose all 14 pathologies better than the baseline and is competitive with other published methods. Our work provides the quantitative results to answer following research questions for the dataset: 1) What loss functions to use for training DCNN from scratch on ChestXray14 dataset that demonstrates high class imbalance and label co occurrence? 2) How to use cascading to model label dependency and to improve accuracy of the deep learning model?
PRISM: An Algorithm for Inducing Modular Rules

The decision tree output of Quinlan's ID3 algorithm is one of its major weaknesses. Not only can it be incomprehensible and difficult to manipulate, but its use in expert systems frequently demands irrelevant information to be supplied. This report argues that the problem lies in the induction algorithm itself and can only be remedied by radically altering the underlying strategy. It describes a new algorithm, PRISM which, although based on ID3, uses a different induction strategy to induce rules which are modular, thus avoiding many of the problems associated with decision trees.
Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller.

Most BCIs have to undergo a calibration session in which data is recorded to train decoders with machine learning. Only recently zero-training methods have become a subject of study. This work proposes a probabilistic framework for BCI applications which exploit event-related potentials (ERPs). For the example of a visual P300 speller we show how the framework harvests the structure suitable to solve the decoding task by (a) transfer learning, (b) unsupervised adaptation, (c) language model and (d) dynamic stopping.



A simulation study compares the proposed probabilistic zero framework (using transfer learning and task structure) to a state-of-the-art supervised model on n = 22 subjects. The individual influence of the involved components (a)-(d) are investigated.



Without any need for a calibration session, the probabilistic zero-training framework with inter-subject transfer learning shows excellent performance--competitive to a state-of-the-art supervised method using calibration. Its decoding quality is carried mainly by the effect of transfer learning in combination with continuous unsupervised adaptation.



A high-performing zero-training BCI is within reach for one of the most popular BCI paradigms: ERP spelling. Recording calibration data for a supervised BCI would require valuable time which is lost for spelling. The time spent on calibration would allow a novel user to spell 29 symbols with our unsupervised approach. It could be of use for various clinical and non-clinical ERP-applications of BCI.
Human–Robot Interaction: Kinematics and Muscle Activity Inside a Powered Compliant Knee Exoskeleton

Until today it is not entirely clear how humans interact with automated gait rehabilitation devices and how we can, based on that interaction, maximize the effectiveness of these exoskeletons. The goal of this study was to gain knowledge on the human-robot interaction, in terms of kinematics and muscle activity, between a healthy human motor system and a powered knee exoskeleton (i.e., KNEXO). Therefore, temporal and spatial gait parameters, human joint kinematics, exoskeleton kinetics and muscle activity during four different walking trials in 10 healthy male subjects were studied. Healthy subjects can walk with KNEXO in patient-in-charge mode with some slight constraints in kinematics and muscle activity primarily due to inertia of the device. Yet, during robot-in-charge walking the muscular constraints are reversed by adding positive power to the leg swing, compensating in part this inertia. Next to that, KNEXO accurately records and replays the right knee kinematics meaning that subject-specific trajectories can be implemented as a target trajectory during assisted walking. No significant differences in the human response to the interaction with KNEXO in low and high compliant assistance could be pointed out. This is in contradiction with our hypothesis that muscle activity would decrease with increasing assistance. It seems that the differences between the parameter settings of low and high compliant control might not be sufficient to observe clear effects in healthy subjects. Moreover, we should take into account that KNEXO is a unilateral, 1 degree-of-freedom device.
UTTime: Temporal Relation Classification using Deep Syntactic Features

In this paper, we present a system, UTTime, which we submitted to TempEval-3 for  Annotating temporal relations. The system uses logistic regression classifiers and exploits features extracted from a deep syntactic parser, including paths between event words in phrase structure trees and their path lengths, and paths between event words in predicateargument structures and their subgraphs. UTTime achieved an F1 score of 34.9 based on the graphed-based evaluation for Task C (ranked 2) and 56.45 for Task C-relationonly (ranked 1) in the TempEval-3 evaluation.
Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison

With the advances in new-generation information technologies, especially big data and digital twin, smart manufacturing is becoming the focus of global manufacturing transformation and upgrading. Intelligence comes from data. Integrated analysis for the manufacturing big data is beneficial to all aspects of manufacturing. Besides, the digital twin paves a way for the cyber-physical integration of manufacturing, which is an important bottleneck to achieve smart manufacturing. In this paper, the big data and digital twin in manufacturing are reviewed, including their concept as well as their applications in product design, production planning, manufacturing, and predictive maintenance. On this basis, the similarities and differences between big data and digital twin are compared from the general and data perspectives. Since the big data and digital twin can be complementary, how they can be integrated to promote smart manufacturing are discussed.
Real time correlation-based stereo: algorithm, implementations and applications

This paper describes some of the work on stereo that has been going on at INRIA in the last four years. The work has concentrated on obtaining dense, accurate, and reliable range maps of the environment at rates compatible with the real-time constraints of such applications as the navigation of mobile vehicles in man-made or natural environments. The class of algorithms which has been selected among several is the class of correlationbased stereo algorithms because they are the only ones that can produce su ciently dense range maps with an algorithmic structure which lends itself nicely to fast implementations because of the simplicity of the underlying computation. We describe the various improvements that we have brought to the original idea, including validation and characterization of the quality of the matches, a recursive implementation of the score computation which makes the method independent of the size of the correlation window, and a calibration method which does not require the use of a calibration pattern. We then describe two implementations of this algorithm on two very di erent pieces of hardware. The rst implementation is on a board with four Digital Signal Processors designed jointly with Matra MSII. This implementation can produce 64 64 range maps at rates varying between 200 and 400 ms, depending upon the range of disparities. The second implementation is on a board developed by DEC-PRL and can perform the cross-correlation of two 256 256 images in 140 ms. The rst implementation has been integrated in the navigation system of the INRIA cart and used to correct for inertial and odometric errors in navigation experiments both indoors and outdoors on road. This is the rst application of our correlation-based algorithm which is described in the paper. The second application has been done jointly with people from the french national space agency (CNES) to study the possibility of using stereo on a future planetary rover for the construction of Digital Elevation Maps. We have shown that real time stereo is possible today at low-cost and can be applied in real applications. The algorithm that has been described is not the most sophisticated available but we have made it robust and reliable thanks to a number of improvements. Even though each of these improvements is not earth-shattering from the pure research point of view, altogether they have allowed us to go beyond a very important threshold. This threshold measures the di erence between a program that runs in the laboratory on a few images and one that works continuously for hours on a sequence of stereo pairs and produces results at such rates and of such quality that they can be used to guide a real vehicle or to produce Discrete Elevation Maps. We believe that this threshold has only been reached in a very small number of cases.
SPrank: Semantic Path-Based Ranking for Top-N Recommendations Using Linked Open Data

In most real-world scenarios, the ultimate goal of recommender system applications is to suggest a short ranked list of items, namely top-N recommendations, that will appeal to the end user. Often, the problem of computing top-N recommendations is mainly tackled with a two-step approach. The system focuses first on predicting the unknown ratings, which are eventually used to generate a ranked recommendation list. Actually, the top-N recommendation task can be directly seen as a ranking problem where the main goal is not to accurately predict ratings but to directly find the best-ranked list of items to recommend. In this article we present SPrank, a novel hybrid recommendation algorithm able to compute top-N recommendations exploiting freely available knowledge in the Web of Data. In particular, we employ DBpedia, a well-known encyclopedic knowledge base in the Linked Open Data cloud, to extract semantic path-based features and to eventually compute top-N recommendations in a learning-to-rank fashion. Experiments with three datasets related to different domains (books, music, and movies) prove the effectiveness of our approach compared to state-of-the-art recommendation algorithms.
Transaction costs and the sharing economy

The paper discusses the so-called sharing economy from an industrial structure perspective. The illustrative cases examined are Airbnb and Uber. The research question raised is concerned with the extent to which transaction cost theory can be used to explain the changing industrial structures in the application areas that the Internet-based platforms are addressing and how other theoretical frameworks can be helpful in understanding these developments. The paper concludes by proposing a theoretical framework for analyzing the structural implications of the sharing economy based on theories on multi-sided platforms, transaction costs, and substitution and complementation.
Neural Networks Approach to the Random Walk Dilemma of Financial Time Series

Predictions of financial time series often show a characteristic one step shift relative to the original data as in a random walk. This has been the cause for opposing views whether such time series do contain information that can be extracted for predictions, or are simply random walks. In this case study, we show that NNs that are capable of extracting weak low frequency periodic signals buried in a strong high frequency signal, consistently predict the next value in the series to be the current value, as in a random walk, when used for one-step-ahead predictions of the detrended S&P 500 time series. In particular for the Time Delay Feed Forward Networks and Elman Networks of various configurations, our study supports the view of the detrended S&P 500 being a random walk series. This is consistent with the long standing hypothesis that some financial time series are random walk series.