nils-herrmann/clean_scirepeval huggingface dataset:
The hymen morphology in normal newborn Saudi girls.

Hymen morphology has a medico-legal importance. In view of the lack of national norms, establishing the hymen morphology of Saudi newborn infants is essential.



Over a period of 4 months, the genitalia of 345 full-term female newborn infants were examined to determine the shape of the hymen. A previously described labia traction technique was used to classify the hymen morphology into annular, sleeve-like, fimbriated, crescentric, and other types.



The hymen was present in all 345 female newborn infants examined. A total of 207 (60%) were of the annular type, 76 (22%) were sleeve-like, 43 (12.5%) fimbriated, 17 (4.9%) crescentric, and 2 (0.6%) of other types.



The most common hymen morphology in Saudi newborn girls was annular, followed by sleeve-like, fimbriated, and crescentric. This study may be the first to define normal configuration of the hymen in this community.
The facial width-to-height ratio determines interpersonal distance preferences in the observer.

Facial width-to-height ratio (fWHR) is correlated with a number of aspects of aggressive behavior in men. Observers appear to be able to assess aggressiveness from male fWHR, but implications for interpersonal distance preferences have not yet been determined. This study utilized a novel computerized stop-distance task to examine interpersonal space preferences of female participants who envisioned being approached by a man; men's faces photographed posed in neutral facial expressions were shown in increasing size to mimic approach. We explored the effect of the men's fWHR, their behavioral aggression (measured previously in a computer game), and women's ratings of the men's aggressiveness, attractiveness, and masculinity on the preferred interpersonal distance of 52 German women. Hierarchical linear modelling confirmed the relationship between the fWHR and trait judgements (ratings of aggressiveness, attractiveness, and masculinity). There were effects of fWHR and actual aggression on the preferred interpersonal distance, even when controlling statistically for men's and the participants' age. Ratings of attractiveness, however, was the most influential variable predicting preferred interpersonal distance. Our results extend earlier findings on fWHR as a cue of aggressiveness in men by demonstrating implications for social interaction. In conclusion, women are able to accurately detect aggressiveness in emotionally neutral facial expressions, and adapt their social distance preferences accordingly.
Location Privacy in Usage-Based Automotive Insurance: Attacks and Countermeasures

Usage-based insurance (UBI) is regarded as a promising way to provide accurate automotive insurance rates by analyzing the driving behaviors (e.g., speed, mileage, and harsh braking/accelerating) of drivers. The best practice that has been adopted by many insurance programs to protect users’ location privacy is the use of driving speed rather than GPS data. However, in this paper, we challenge this approach by presenting a novel speed-based location trajectory inference framework. The basic strategy of the proposed inference framework is motivated by the following observations. In practice, many environmental factors, such as real-time traffic and traffic regulations, can influence the driving speed. These factors provide side-channel information about the driving route, which can be exploited to infer the vehicle’s trace. We implement our discovered attack on a public data set in New Jersey. The experimental results show that the attacker has a nearly 60% probability of obtaining the real route if he chooses the top 10 candidate routes. To thwart the proposed attack, we design a privacy preserving scoring and data audition framework that enhances drivers’ control on location privacy without affecting the utility of UBI. Our defense framework can also detect users’ dishonest behavior (e.g., modification of speed data) via a probabilistic audition scheme. Extensive experimental results validate the effectiveness of the defense framework.
The influence of brand image and company reputation where manufacturers market to small firms : A customer value perspective

Branding research has largely focused on consumer goods markets and only recently has attention been given to business markets. In many business markets the company’s reputation has a strong influence on buying decisions which may differ from the more specific product related influence of the brand’s image. In this paper we investigate these differences by testing the hypotheses about the influences of brand image and company reputation on customers’ perceptions of product and service quality, customer value, and customer loyalty in a business market where there are three manufacturers marketing their brands directly to a large number of small firms. The results indicate that the brand’s image has a more specific influence on the customers’ perceptions of product and service quality while the company’s reputation has a broader influence on perceptions of customer value and customer loyalty. D 2005 Elsevier Inc. All rights reserved.
Restricted and repetitive behaviors in toddlers and preschoolers with autism spectrum disorders based on the Autism Diagnostic Observation Schedule (ADOS).

Restricted and repetitive behaviors (RRBs) observed during the Autism Diagnostic Observation Schedule [ADOS: Lord et al., 2000] were examined in a longitudinal data set of 455 toddlers and preschoolers (age 8-56 months) with clinical diagnosis of Autism Spectrum Disorders (ASD; autism, n=121 and pervasive developmental disorders-not otherwise specified (PDD-NOS), n=71), a nonspectrum disorder (NS; n=90), or typical development (TD; n=173). Even in the relatively brief semi-structured observations, GEE analyses of the severity and prevalence of RRBs differentiated children with ASD from those with NS and TD across all ages. RRB total scores on the ADOS were stable over time for children with ASD and NS; however, typically developing preschoolers showed lower RRB scores than typically developing toddlers. Nonverbal IQ (NVIQ) was more strongly related to the prevalence of RRBs in older children with PDD-NOS, NS, and TD than younger children under 2 years and those with autism. Item analyses revealed different relationships between individual items and NVIQ, age, diagnosis, and gender. These findings are discussed in terms of their implications for the etiology and treatment of RRBs as well as for the framework of ASD diagnostic criteria in future diagnostic systems.
Theoretical framework for physiological profiling using sensors and Big Data Analytics

In today's world, everybody has limited time and generally do rash driving to reach at destination. So intentionally or unintentionally many crashes occurred and generally get mentally upset which leads to more crime or health disturbance. Due to work pressure and unavoidable environment, some changes in hormones have been occurred. This paper is based on analysis of physiological profile of drivers with the help of smart phone based application. Sensors will collect the real time in vehicle data which would be analyzed with the help of Big Data Analytics. Driver's behavior is affected by number of factors. This paper will discuss about that number of factors which affects the nature of a person. We examine an algorithm to check whether the behavior of driver is normal or abnormal. This will help the administration to analyze the chances of crashes or crimes that may occurred due to abnormal behavior and to some extent, things can be controlled.
Process Mining on Databases: Unearthing Historical Data from Redo Logs

Process Mining techniques rely on the existence of event data. However, in many cases it is far from trivial to obtain such event data. Considerable efforts may need to be spent on making IT systems record historic data at all. But even if such records are available, it may not be possible to derive an event log for the case notion one is interested in, i.e., correlating events to form process instances may be challenging. This paper proposes an approach that exploits a commonly available and versatile source of data, i.e. database redo logs. Such logs record the writing operations performed in a general-purpose database for a range of objects, which constitute a collection of events. By using the relations between objects as specified in the associated data model, it is possible to turn such events into an event log for a wide range of case types. The resulting logs can be analyzed using existing process mining techniques.
Facial Landmark Tracking and Facial Expression Recognition

In this paper, we address the challenging computer vision problem of obtaining a reliable facial expression analysis from a naturally interacting person. We propose a system that combines a 3D generic face model, 3D head tracking, and 2D tracker to track facial landmarks and recognize expressions. First, we extract facial landmarks from a neutral frontal face, and then we deform a 3D generic face to fit the input face. Next, we use our real-time 3D head tracking module to track a person’s head in 3D and predict facial landmark positions in 2D using the projection from the updated 3D face model. Finally, we use tracked 2D landmarks to update the 3D landmarks. This integrated tracking loop enables efficient tracking of the non-rigid parts of a face in the presence of large 3D head motion. We conducted experiments for facial expression recognition using both framebased and sequence-based approaches. Our method provides a 75.9% recognition rate in 8 subjects with 7 key expressions. Our approach provides a considerable step forward toward new applications including human-computer interactions, behavioral science, robotics, and game applications.
Internet addiction in Korean adolescents and its relation to depression and suicidal ideation: a questionnaire survey.

This study examined the relationship of Internet addiction to depression and suicidal ideation in Korean adolescents. The participants were 1573 high-school students living in a city who completed the self-reported measures of the Internet Addiction Scale, the Korean version of the Diagnostic Interview Schedule for Children-Major Depression Disorder-Simple Questionnaire, and the Suicidal Ideation Questionnaire-Junior. A correlational survey design was employed. Among the samples, 1.6% was diagnosed as Internet addicts, while 38.0% was classified as possible Internet addicts. The prevalence of Internet addiction did not vary with gender. The levels of depression and suicide ideation were highest in the Internet-addicts group. Future studies should investigate the direct relationship between psychological health problems and Internet dependency.
A Wideband Dual-Polarized Dielectric Magnetoelectric Dipole Antenna

A new wideband ±45° dual-polarized metal loop dielectric resonator magnetoelectric antenna is proposed in this communication. The antenna consists of two orthogonal dielectric bars that support two orthogonal electric dipoles and two cross interlaced metal semi-loops that are equivalent to two orthogonal magnetic dipoles above the ground. With a legitimate combination of the electric and magnetic dipoles, a unidirectional radiation with low backward radiation and equal E-plane and H-plane radiation patterns can be achieved. The antenna can be made of a monolithic dielectric block with simple installation. To validate the new antenna configuration, a prototype is designed, manufactured, and measured. The prototype antenna using ceramic with the electrical size of <inline-formula> <tex-math notation="LaTeX">$0.33\lambda _{o} \times 0.33\lambda _{o} \times 0.21\lambda _{o}$ </tex-math></inline-formula> demonstrates that an impedance bandwidth of 11.4% (<inline-formula> <tex-math notation="LaTeX">$\vert S_{11}\vert < -15$ </tex-math></inline-formula> dB) in the 3.5 GHz frequency band can be achieved with the measured forward/backward ratio of better than 22 dB. The proposed antenna is suitable as antenna element for massive-MIMO array antennas, where a large number of antenna elements need to be installed in a compact space.
An Algorithm for image stitching and blending

In many clinical studies, including those of cancer, it is highly desirable to acquire images of whole tumour sections whilst retaining a microscopic resolution. A usual approach to this is to create a composite image by appropriately overlapping individual images acquired at high magnification under a microscope. A mosaic of these images can be accurately formed by applying image registration, overlap removal and blending techniques. We describe an optimised, automated, fast and reliable method for both image joining and blending. These algorithms can be applied to most types of light microscopy imaging. Examples from histology, from in vivo vascular imaging and from fluorescence applications are shown, both in 2D and 3D. The algorithms are robust to the varying image overlap of a manually moved stage, though examples of composite images acquired both with manually-driven and computer-controlled stages are presented. The overlap-removal algorithm is based on the cross-correlation method; this is used to determine and select the best correlation point between any new image and the previous composite image. A complementary image blending algorithm, based on a gradient method, is used to eliminate sharp intensity changes at the image joins, thus gradually blending one image onto the adjacent ‘composite’. The details of the algorithm to overcome both intensity discrepancies and geometric misalignments between the stitched images will be presented and illustrated with several examples.
Regulation of wound healing by growth factors and cytokines.

Cutaneous wound healing is a complex process involving blood clotting, inflammation, new tissue formation, and finally tissue remodeling. It is well described at the histological level, but the genes that regulate skin repair have only partially been identified. Many experimental and clinical studies have demonstrated varied, but in most cases beneficial, effects of exogenous growth factors on the healing process. However, the roles played by endogenous growth factors have remained largely unclear. Initial approaches at addressing this question focused on the expression analysis of various growth factors, cytokines, and their receptors in different wound models, with first functional data being obtained by applying neutralizing antibodies to wounds. During the past few years, the availability of genetically modified mice has allowed elucidation of the function of various genes in the healing process, and these studies have shed light onto the role of growth factors, cytokines, and their downstream effectors in wound repair. This review summarizes the results of expression studies that have been performed in rodents, pigs, and humans to localize growth factors and their receptors in skin wounds. Most importantly, we also report on genetic studies addressing the functions of endogenous growth factors in the wound repair process.
A Proximity-Aware Hierarchical Clustering of Faces

In this paper, we propose an unsupervised face clustering algorithm called “Proximity-Aware Hierarchical Clustering” (PAHC) that exploits the local structure of deep representations. In the proposed method, a similarity measure between deep features is computed by evaluating linear SVM margins. SVMs are trained using nearest neighbors of sample data, and thus do not require any external training data. Clus- ters are then formed by thresholding the similarity scores. We evaluate the clustering performance using three challenging un- constrained face datasets, including Celebrity in Frontal-Profile (CFP), IARPA JANUS Benchmark A (IJB-A), and JANUS Challenge Set 3 (JANUS CS3) datasets. Experimental results demonstrate that the proposed approach can achieve significant improvements over state-of-the-art methods. Moreover, we also show that the proposed clustering algorithm can be applied to curate a set of large-scale and noisy training dataset while maintaining sufficient amount of images and their variations due to nuisance factors. The face verification performance on JANUS CS3 improves significantly by finetuning a DCNN model with the curated MS-Celeb-1M dataset which contains over three million face images.
Data Processing on FPGAs

Roughly a decade ago, power consumption and heat dissipation concerns forced the semiconductor industry to radically change its course, shifting from sequential to parallel computing. Unfortunately, improving performance of applications has now become much more difficult than in the good old days of frequency scaling. is is also affecting databases and data processing applications in general, and has led to the popularity of so-called data appliances—specialized data processing engines, where software and hardware are sold together in a closed box. Fieldprogrammable gate arrays (FPGAs) increasingly play an important role in such systems. FPGAs are attractive because the performance gains of specialized hardware can be significant, while power consumption is much less than that of commodity processors. On the other hand, FPGAs are way more flexible than hard-wired circuits (ASICs) and can be integrated into complex systems inmany different ways, e.g., directly in the network for a high-frequency trading application. is book gives an introduction to FPGA technology targeted at a database audience. In the first few chapters, we explain in detail the inner workings of FPGAs. en we discuss techniques and design patterns that help mapping algorithms to FPGA hardware so that the inherent parallelism of these devices can be leveraged in an optimal way. Finally, the book will illustrate a number of concrete examples that exploit different advantages of FPGAs for data processing.
Web-Based Live Speech-Driven Lip-Sync

Virtual characters are an integral part of many games and virtual worlds. The ability to accurately synchronize lip movement to audio speech is an important aspect in the believability of the character. In this paper we propose a simple rule-based lip-syncing algorithm for virtual agents using the web browser. It works in real-time with live input, unlike most current lip-syncing proposals, which may require considerable amounts of computation, expertise and time to set up. Our method generates reliable speech animation based on live speech using three blend shapes and no training, and it only needs manual adjustment of three parameters for each speaker (sensitivity, smoothness and vocal tract length). Our proposal is based on the limited real-time audio processing functions of the client web browser (thus, the algorithm needs to be simple), but this facilitates the use of web based embodied conversational agents.
A survey of technical trend of ADAS and autonomous driving

For past 10 years Advanced Driving Assistance System (ADAS) has rapidly grown. Recently not only luxury cars but some entry level cars are equipped with ADAS applications, such as Automated Emergency Braking System (AEBS). The European New Car Assessment Programme (EuroNCAP) announced its introduction of AEBS test from 2014, which will accelerate the penetration of ADAS in Europe. Also DARPA challenge started from 2004 accelerated the research for autonomous driving. Several OEMs and universities have demonstrated autonomous driving cars. This paper gives a brief survey of technical trend of ADAS and autonomous driving focusing on algorithms actually used for autonomous driving prototype cars.
Modeling driver behavior at roundabouts: Results from a field study

Advanced Driving Assistance Systems could improve driving safety and comfort by supporting drivers in their driving task. To realize intelligent assistance, driver behavior prediction and recognition is an important challenge. Therefore, the aim of this study is to develop a method to predict whether a vehicle, having entered a roundabout, will choose an upcoming exit or stay within the roundabout. A field study has been conducted to collect driving behavior data for analyzing and modeling human driver behavior in interaction with roundabouts. Support vector machines proved to be a robust and efficient classification method for the roundabout leaving/ staying pattern recognition problem. From the experimental results the vehicles position can be estimated, for which the prediction becomes reliable. The steering wheel angle and angle velocity also proved to be able to provide sufficient information to predict the driver behavior at the investigated roundabouts.
Understanding Online Payment Method Choice: An Eye-tracking Study

Due to the impact of online payment on the development of e-commerce, this article seeks to deepen the current understanding about the determinants of online payment method choice. Based on an extensive literature review, we identified perceived trustworthiness of the seller and perceived product uncertainty as major determinants, and we theorize that product type, product price, and product description are antecedents of product uncertainty. In our theoretical framework, we model perceived trustworthiness of the seller and perceived product uncertainty as independent variables, which are hypothesized to predict the dependent variable, namely online payment method choice (credit card, debit card, or cash on delivery). Moreover, we define payment method characteristics (e.g., information security) and buyer characteristics (e.g., trust propensity, online shopping experience) as control variables. Also, we describe a laboratory experiment in which we test our theoretical framework. Considering the recent calls for the use of neurobiological and physiological approaches to advance information systems (IS) theorizing (see, for example, www.NeuroIS.org), we suggest using eye-tracking data to complement traditional data sources, particularly those captured through survey research. Specifically, we propose that eyetracking data can be used to measure product uncertainty, a major predictor of online payment method choice, which is associated with unconscious and automatic information processing that cannot be articulated easily through self-reports.
Sentiment Analysis using Word-Graphs

The Word-Graph Sentiment Analysis Method is proposed to identify the sentiment that expressed in a microblog document using the sequence of the words that contains. The sequence of the words can be represented using graphs in which graph similarity metrics and classification algorithms can be applied to produce sentiment predictions. Experiments that were carried out with this method in a Twitter dataset validate the proposed model and allow us to further understand the metrics and the criteria that can be applied in words-graphs to predict the sentiment disposition of short, microblog documents.