nils-herrmann/clean_scirepeval huggingface dataset:
A Pattern Learning Approach to Question Answering Within the Ephyra Framework

This paper describes the Ephyra question answering engine, a modular and extensible framework that allows to integrate multiple approaches to question answering in one system. Our framework can be adapted to languages other than English by replacing language-specific components. It supports the two major approaches to question answering, knowledge annotation and knowledge mining. Ephyra uses the web as a data resource, but could also work with smaller corpora. In addition, we propose a novel approach to question interpretation which abstracts from the original formulation of the question. Text patterns are used to interpret a question and to extract answers from text snippets. Our system automatically learns the patterns for answer extraction, using question-answer pairs as training data. Experimental results revealed the potential of this approach.
Developing a Web-Based Application using OWL and SWRL

Data integration is central in Web application development because these applications typically deal with a variety of information formats. Ontology-driven applications face the additional challenge of integrating these multiple formats with the information stored in ontologies. A number of mappings are required to reconcile the variety of formats to produce a coherent overall system. To address these mappings we have developed a number of open source tools that support transformations between some of the common formats encountered when developing an ontology-driven Web application. The Semantic Web Rule Language (SWRL) is a central building block in these tools. We describe these tools and illustrate their use in the development of a prototype Web-based application.
Online consumer retention: contingent effects of online shopping habit and online shopping experience

28 January 2005  August 2005  January 2007 4th Revision: 14 June 2007  20 September 2007  In this study, we further develop the information systems continuance model in the context of online shopping, using a contingency theory that accounts for the roles of online shopping habit and online shopping experience. Specifically, we argue and empirically demonstrate that although conceptually distinct, online shopping habit and online shopping experience have similar effects on repurchase intention. They both have positive mediated effects through satisfaction and moderate the relationship between satisfaction and online repurchase intention. The results of a survey study involving 122 online customers provide strong support for our research model. We also identify after-sale service, transaction efficiency, security, convenience, and cost savings as important online shopping usefulness drivers. Theoretical and practical implications include establishing a contingency theory to more fully explain online customer retention as well as guidelines for development of customer relationship management initiatives.  (2007) 16, 780–792.
Ovariohysterectomy versus ovariectomy for elective sterilization of female dogs and cats: is removal of the uterus necessary?

Views: Commentary 1409 E sterilization of female dogs and cats is one of the most common procedures performed in veterinary practice and is considered by private veterinary practitioners as one of the most important skills required of new graduates. Potential benefits of sterilization include population control, prevention of diseases of the reproductive tract, and elimination of undesirable behaviors associated with hormonal cycling. The AVMA and the Association of Shelter Veterinarians both promote elective sterilization of female dogs and cats as integral to reducing euthanasia of unwanted dogs and cats. Sterilization of female dogs and cats can be accomplished by removing both the ovaries and uterus (ovariohysterectomy) or by removing the ovaries alone (ovariectomy). Ovariohysterectomy has historically been recommended in the United States and Canada and is currently emphasized at schools and colleges of veterinary medicine in these countries. To our knowledge, only one of the most frequently used surgical textbooks in the United States and Canada describes ovariectomy, and ovariectomy is generally not taught to veterinary students during their surgical laboratories. The Clinical Proficiency Examination, which is the final step in the Educational Commission for Foreign Veterinary Graduates’ program for establishing the educational equivalence of graduates of nonaccredited veterinary schools seeking licensure in the United States or Canada, requires “complete removal of both ovaries and removal of the majority of the uterus” to obtain a passing grade. Despite the apparent preference for ovariohysterectomy in the United States and Canada, ovariectomy appears to have become the standard of care in many European countries. In addition, with the development of minimally invasive surgical techniques, laparoscopic ovariectomy has gained popularity. Thus, it may be helpful to review the scientific evidence comparing ovariohysterectomy and ovariectomy for elective sterilization of healthy female dogs and cats.
Social Media Use in the United States: Implications for Health Communication

Given the rapid changes in the communication landscape brought about by participative Internet use and social media, it is important to develop a better understanding of these technologies and their impact on health communication. The first step in this effort is to identify the characteristics of current social media users. Up-to-date reporting of current social media use will help monitor the growth of social media and inform health promotion/communication efforts aiming to effectively utilize social media.



The purpose of the study is to identify the sociodemographic and health-related factors associated with current adult social media users in the United States.



Data came from the 2007 iteration of the Health Information National Trends Study (HINTS, N = 7674). HINTS is a nationally representative cross-sectional survey on health-related communication trends and practices. Survey respondents who reported having accessed the Internet (N = 5078) were asked whether, over the past year, they had (1) participated in an online support group, (2) written in a blog, (3) visited a social networking site. Bivariate and multivariate logistic regression analyses were conducted to identify predictors of each type of social media use.



Approximately 69% of US adults reported having access to the Internet in 2007. Among Internet users, 5% participated in an online support group, 7% reported blogging, and 23% used a social networking site. Multivariate analysis found that younger age was the only significant predictor of blogging and social networking site participation; a statistically significant linear relationship was observed, with younger categories reporting more frequent use. Younger age, poorer subjective health, and a personal cancer experience predicted support group participation. In general, social media are penetrating the US population independent of education, race/ethnicity, or health care access.



Recent growth of social media is not uniformly distributed across age groups; therefore, health communication programs utilizing social media must first consider the age of the targeted population to help ensure that messages reach the intended audience. While racial/ethnic and health status-related disparities exist in Internet access, among those with Internet access, these characteristics do not affect social media use. This finding suggests that the new technologies, represented by social media, may be changing the communication pattern throughout the United States.
Comic image understanding based on polygon detection

Comic image understanding aims to automatically decompose scanned comic page images into storyboards and then identify the reading order of them, which is the key technique to produce digital comic documents that are suitable for reading on mobile devices. In this paper, we propose a novel comic image understanding method based on polygon detection. First, we segment a comic page images into storyboards by finding the polygonal enclosing box of each storyboard. Then, each storyboard can be represented by a polygon, and the reading order of them is determined by analyzing the relative geometric relationship between each pair of polygons. The proposed method is tested on 2000 comic images from ten printed comic series, and the experimental results demonstrate that it works well on different types of comic images.
Improvement of Marching Cubes Algorithm Based on Sign Determination

Traditional Marching Cubes algorithm has the problem of repeating calculation, so that an improved Marching Cubes algorithm is put forward. Boundary voxel is utilized to find adjacent boundary voxel. According to the relationship of edge and edge sign between boundary voxel and adjacent boundary voxel, we transmit intersection on the common face of the boundary voxel to adjacent boundary voxel. If the edge sign of adjacent boundary voxel is not existed, we change the edge sign of the adjacent boundary voxel simultaneously. In that way, we can avoid double counting of the intersection, which is in two adjacent boundary voxels. The two adjacent boundary voxels have common surface on where the edge of the isosurface lies. During the computation of intersection of edge and isosurface, we only compute the edges whose edge signs are null. At the same time, we make use of edge sign to avoid repeating assignment. It speeds up isosurface extraction.
GPU Accelerated Sub-Sampled Newton's Method

First order methods, which solely rely on gradient information, are commonly used in diverse machine learning (ML) and data analysis (DA) applications. This is attributed to the simplicity of their implementations, as well as low per-iteration computational/storage costs. However, they suffer from significant disadvantages; most notably, their performance degrades with increasing problem ill-conditioning. Furthermore, they often involve a large number of hyper-parameters, and are notoriously sensitive to parameters such as the step-size. By incorporating additional information from the Hessian, second-order methods, have been shown to be resilient to many such adversarial effects. However, these advantages of using curvature information come at the cost of higher per-iteration costs, which in “big data” regimes, can be computationally prohibitive. In this paper, we show that, contrary to conventional belief, second-order methods, when implemented appropriately, can be more efficient than first-order alternatives in many largescale ML/ DA applications. In particular, in convex settings, we consider variants of classical Newton’s method in which the Hessian and/or the gradient are randomly sub-sampled. We show that by effectively leveraging the power of GPUs, such randomized Newton-type algorithms can be significantly accelerated, and can easily outperform state of the art implementations of existing techniques in popular ML/ DA software packages such as TensorFlow. Additionally these randomized methods incur a small memory overhead compared to first-order methods. In particular, we show that for million-dimensional problems, our GPU accelerated sub-sampled Newton’s method achieves a higher test accuracy in milliseconds as compared with tens of seconds for first order alternatives.
Medico legal procedures related to sexual assault : a 10-year retrospective experience of a Daphne protocol application

Sexual assault is a worldwide problem that has not yet been sufficiently acknowledged as confirmed by the literature. Italian law n.96, 1996, foreseeing norms regarding rape and sexual abuse, finally gave significant relevance to sex crimes. In 2004, the European Commission for Justice Internal Affairs and Social Politics promoted the Daphne II program to support victims of rape and abuse, and the Violence and Operative Healthcare Networks (Ve.R.S.O.) project started at the Policlinico “P. Giaccone” University Hospital of Palermo in 2006.  data analysis emerging from 10 years experience of Daphne protocol utilization for the management of sexual assault victims.  From October 2006 since December 2016 a total of 90 victims of sexual assault were retrospectively investigated. Patients are divided into groups in relation to: gender, age, place of SA, number and type of assailant, nature of sexual assault, presence/absence of physical or genital injuries.  Among victims 88 were females (97%) and 2 males (3%); 68 Italians (75.5%) and 22 foreigners (24.5%). At the time the events occurred, 42% (n = 38) of the victims were minors aged less than 16 years. In 11 cases, the age of the victim is not indicated. The assailant was an acquaintance of the victim in 65% of the cases (in 73% of these cases, the assailant was a family member). In 26 cases (28%) happened indoor, 44 cases happened outdoor and in 20 cases there were no data. Evidence of recent acute general body trauma (abrasions, bruises, lacerations) was found in 38 cases (42%); other types of injuries include: genital trauma (14 cases, 15%), genital and body traumas (49 cases, 54%) (tab. 1–2-). In 24 cases (26%) there were no injuries. Among genital trauma, we distinguished vulvovaginal lesions (68.5%) and anal lesions (31.5%). We have classified the minor victims using first Adam’s classification and based on Adam’s classification revised in 2015.  Application of the Ve.R.S.O project protocol changed and greatly improved health management of victims of violence. By following these procedures, violence and abuse are analyzed from every point of view, also for an appropriate assessment of the medium and long term health consequences of sexual assault. Only in this way are citizens provided with a high level of protection against gender violence, psychological support and prevention from any form of violence which takes place in respect of such vulnerable people.
Feature Selection using Multiobjective Optimization for Aspect based Sentiment Analysis

In this paper, we propose a system for aspect-based sentiment analysis (ABSA) by incorporating the concepts of multi-objective optimization (MOO), distributional thesaurus (DT) and unsupervised lexical induction. The task can be thought of as a sequence of processes such as aspect term extraction, opinion target expression identification and sentiment classification. We use MOO for selecting the most relevant features, and demonstrate that classification with the resulting feature set can improve classification accuracy on many datasets. As base learning algorithms we make use of Support Vector Machines (SVM) for sentiment classification and Conditional Random Fields (CRF) for aspect term and opinion target expression extraction tasks. Distributional thesaurus and unsupervised DT prove to be effective with enhanced performance. Experiments on benchmark setups of SemEval-2014 and SemEval-2016 shared tasks show that we achieve the state of the art on aspect-based sentiment analysis for several languages.
Individual ball possession in soccer

This paper describes models for detecting individual and team ball possession in soccer based on position data. The types of ball possession are classified as Individual Ball Possession (IBC), Individual Ball Action (IBA), Individual Ball Control (IBC), Team Ball Possession (TBP), Team Ball Control (TBC) und Team Playmaking (TPM) according to different starting points and endpoints and the type of ball control involved. The machine learning approach used is able to determine how long the ball spends in the sphere of influence of a player based on the distance between the players and the ball together with their direction of motion, speed and the acceleration of the ball. The degree of ball control exhibited during this phase is classified based on the spatio-temporal configuration of the player controlling the ball, the ball itself and opposing players using a Bayesian network. The evaluation and application of this approach uses data from 60 matches in the German Bundesliga season of 2013/14, including 69,667 IBA intervals. The identification rate was F = .88 for IBA and F = .83 for IBP, and the classification rate for IBC was κ = .67. Match analysis showed the following mean values per match: TBP 56:04 ± 5:12 min, TPM 50:01 ± 7:05 min and TBC 17:49 ± 8:13 min. There were 836 ± 424 IBC intervals per match and their number was significantly reduced by -5.1% from the 1st to 2nd half. The analysis of ball possession at the player level indicates shortest accumulated IBC times for the central forwards (0:49 ± 0:43 min) and the longest for goalkeepers (1:38 ± 0:58 min), central defenders (1:38 ± 1:09 min) and central midfielders (1:27 ± 1:08 min). The results could improve performance analysis in soccer, help to detect match events automatically, and allow discernment of higher value tactical structures, which is based on individual ball possession.
A new resource for incremental, dependency and semantic parsing

We introduce LTAG-spinal, a novel variant of traditional Lexicalized Tree Adjoining Grammar (LTAG) with desirable linguistic, computational and statistical properties. Unlike in traditional LTAG, subcategorization frames and the argument-adjunct distinction are left underspecified in LTAG-spinal. LTAG-spinal with adjunction constraints is weakly equivalent to LTAG. The LTAG-spinal formalism is used to extract an LTAG-spinal Treebank from the Penn Treebank with Propbank annotation. Based on Propbank annotation, predicate coordination and LTAG adjunction structures are successfully extracted. The LTAG-spinal Treebank makes explicit semantic relations that are implicit or absent from the original PTB. LTAG-spinal provides a very desirable resource for statistical LTAG parsing, incremental parsing, dependency parsing, and semantic parsing. This treebank has been successfully used to train an incremental LTAG-spinal parser and a bidirectional LTAG dependency parser.
A Blockchain Connected Gateway for BLE-Based Devices in the Internet of Things

Recently, the popularity of the Internet of Things (IoT) has led to a rapid development and significant advancement of ubiquitous applications seamlessly integrated within our daily life. Owing to the accompanying growth of the importance of privacy, a great deal of attention has focused on the issues of secure management and robust access control of IoT devices. In this paper, we propose the design of a blockchain connected gateway which adaptively and securely maintains user privacy preferences for IoT devices in the blockchain network. Individual privacy leakage can be prevented because the gateway effectively protects users’ sensitive data from being accessed without their consent. A robust digital signature mechanism is proposed for the purposes of authentication and secure management of privacy preferences. Furthermore, we adopt the blockchain network as the underlying architecture of data processing and maintenance to resolve privacy disputes.
Face Detection using Digital Image Processing

This paper presents a technique for automatically detecting human faces in digital color images. This is two-step process which first detects regions contain human skin in the color image and then extracts information from these regions which might indicate the location of a face in the image. The skin detection is performed using a skin filter which relies on color and texture information. The face detection is performed on a grayscale image containing only the detected skin areas. A combination of thresh holding and mathematical morphology are used to extract object features that would indicate the presence of a face. The face detection process works predictably and fairly reliably, as test results show.
Energy-Efficient Hybrid Analog and Digital Precoding for MmWave MIMO Systems With Large Antenna Arrays

Millimeter wave (mmWave) MIMO will likely use hybrid analog and digital precoding, which uses a small number of RF chains to reduce the energy consumption associated with mixed signal components like analog-to-digital components not to mention baseband processing complexity. However, most hybrid precoding techniques consider a fully connected architecture requiring a large number of phase shifters, which is also energy-intensive. In this paper, we focus on the more energy-efficient hybrid precoding with subconnected architecture, and propose a successive interference cancelation (SIC)-based hybrid precoding with near-optimal performance and low complexity. Inspired by the idea of SIC for multiuser signal detection, we first propose to decompose the total achievable rate optimization problem with nonconvex constraints into a series of simple subrate optimization problems, each of which only considers one subantenna array. Then, we prove that maximizing the achievable subrate of each subantenna array is equivalent to simply seeking a precoding vector sufficiently close (in terms of Euclidean distance) to the unconstrained optimal solution. Finally, we propose a low-complexity algorithm to realize SIC-based hybrid precoding, which can avoid the need for the singular value decomposition (SVD) and matrix inversion. Complexity evaluation shows that the complexity of SIC-based hybrid precoding is only about 10% as complex as that of the recently proposed spatially sparse precoding in typical mmWave MIMO systems. Simulation results verify that SIC-based hybrid precoding is near-optimal and enjoys higher energy efficiency than the spatially sparse precoding and the fully digital precoding.
Estimating the relationship between isoseismal area and earthquake magnitude by a hybrid fuzzy-neural-network method

Utilizing information diffusion method and artificial neural networks, we propose in this paper a hybrid fuzzy neural network to estimate the relationship between isoseismal area and earthquake magnitude. We focus on the study of incompleteness and contradictory nature of patterns in scanty historical earthquake records. Information diffusion method is employed to construct fuzzy relationships which are equal to the number of observations. Integration of the relationships can change the contradictory patterns into more compatible ones which, in turn, can smoothly and quickly train the feedforward neural network with backpropagation algorithm (BP) to obtain the final relationship. A practical application is employed to show the superiority of the model.
Social Media, Big Data, and Public Health Informatics: Ruminating Behavior of Depression Revealed through Twitter

Undiagnosed and untreated depressive disorders have become a serious public health issue and it is prevalent among people of all ages, gender and race. Social media sites, such as Twitter, have become a major venue for people to express/disclose their thoughts and feelings. The tweets from these micro-blogging sites could be used to screen for and potentially detect depression. To date, studies in this area have focused on developing and validating the terms and vocabulary used by users with depression, or evaluating tweets related to depression by using terms that are synonymous with depression. This approach has not produced reliable findings. In this study, we depart from this approach and instead, base our analysis on research on depressive disorders, which indicates the critical significance of repetitive thoughts and ruminating behavior of people with depression. The current study and findings hold important implications for research on depression, social media, and public health informatics.