nils-herrmann/clean_scirepeval huggingface dataset:
Pulse pressure and mean arterial pressure in relation to ischemic stroke among patients with uncontrolled hypertension in rural areas of China.

Information has been sparse on the comparison of pulse pressure (PP) and mean arterial pressure (MAP) in relation to ischemic stroke among patients with uncontrolled hypertension. The present study examined the relation among PP, MAP, and ischemic stroke in uncontrolled hypertensive subjects in China.



A total of 6104 uncontrolled hypertensive subjects aged > or = 35 years were screened with a stratified cluster multistage sampling scheme in Fuxin county of Liaoning province of China, of which 317 had ischemic stroke.



After multivariable adjustment for age, gender, and other confounders, individuals with the highest quartile of PP and MAP had ORs for ischemic stroke of 1.479 (95% CI: 1.027 to 2.130) and 2.000 (95% CI: 1.373 to 2.914) with the lowest quartile as the reference. Adjusted ORs for ischemic stroke were 1.306 for MAP and 1.118 for PP with an increment of 1 SD, respectively. Ischemic stroke prediction of PP was annihilated when PP and MAP were entered in a single model. In patients aged < 65 years, on a continuous scale using receive operating characteristics curve, ischemic stroke was predicted by PP (P=0.001) and MAP (P<0.001). The area under the curve of PP (0.570, 95% CI: 0.531 to 0.609) differed from the area under the curve of MAP (0.633, 95% CI: 0.597 to 0.669; P<0.05). Among patients aged > or = 65 years, presence of ischemic stroke was only predicted by MAP.



PP and MAP were both associated with ischemic stroke. Ischemic stroke prediction of PP depended on MAP. On a continuous scale, MAP better predicted ischemic stroke than PP did in diagnostic accuracy.
Guidelines on the Use of Therapeutic Apheresis in Clinical Practice-Evidence-Based Approach from the Writing Committee of the American Society for Apheresis: The Seventh Special Issue.

The American Society for Apheresis (ASFA) Journal of Clinical Apheresis (JCA) Special Issue Writing Committee is charged with reviewing, updating, and categorizing indications for the evidence-based use of therapeutic apheresis in human disease. Since the 2007 JCA Special Issue (Fourth Edition), the Committee has incorporated systematic review and evidence-based approaches in the grading and categorization of apheresis indications. This Seventh Edition of the JCA Special Issue continues to maintain this methodology and rigor to make recommendations on the use of apheresis in a wide variety of diseases/conditions. The JCA Seventh Edition, like its predecessor, has consistently applied the category and grading system definitions in the fact sheets. The general layout and concept of a fact sheet that was used since the fourth edition has largely been maintained in this edition. Each fact sheet succinctly summarizes the evidence for the use of therapeutic apheresis in a specific disease entity. The Seventh Edition discusses 87 fact sheets (14 new fact sheets since the Sixth Edition) for therapeutic apheresis diseases and medical conditions, with 179 indications, which are separately graded and categorized within the listed fact sheets. Several diseases that are Category IV which have been described in detail in previous editions and do not have significant new evidence since the last publication are summarized in a separate table. The Seventh Edition of the JCA Special Issue serves as a key resource that guides the utilization of therapeutic apheresis in the treatment of human disease.
Visual Storytelling

We introduce the first dataset for sequential vision-to-language, and explore how this data may be used for the task of visual storytelling. The dataset includes 81,743 unique photos in 20,211 sequences, aligned to descriptive and story language. We establish several strong baselines for the storytelling task, and motivate an automatic metric to benchmark progress. We argue that modelling figurative and social language, as provided for in this data and the storytelling task, has the potential to move artificial intelligence towards more human-like expression and understanding.
Machine Recognition of Human Activities: A Survey

The past decade has witnessed a rapid proliferation of video cameras in all walks of life and has resulted in a tremendous explosion of video content. Several applications such as content-based video annotation and retrieval, highlight extraction and video summarization require recognition of the activities occurring in the video. The analysis of human activities in videos is an area with increasingly important consequences from security and surveillance to entertainment and personal archiving. Several challenges at various levels of processing-robustness against errors in low-level processing, view and rate-invariant representations at midlevel processing and semantic representation of human activities at higher level processing-make this problem hard to solve. In this review paper, we present a comprehensive survey of efforts in the past couple of decades to address the problems of representation, recognition, and learning of human activities from video and related applications. We discuss the problem at two major levels of complexity: 1) "actions" and 2) "activities." "Actions" are characterized by simple motion patterns typically executed by a single human. "Activities" are more complex and involve coordinated actions among a small number of humans. We will discuss several approaches and classify them according to their ability to handle varying degrees of complexity as interpreted above. We begin with a discussion of approaches to model the simplest of action classes known as atomic or primitive actions that do not require sophisticated dynamical modeling. Then, methods to model actions with more complex dynamics are discussed. The discussion then leads naturally to methods for higher level representation of complex activities.
Robust Lexical Features for Improved Neural Network Named-Entity Recognition

Neural network approaches to Named-Entity Recognition reduce the need for carefully handcrafted features. While some features do remain in state-of-the-art systems, lexical features have been mostly discarded, with the exception of gazetteers. In this work, we show that this is unfair: lexical features are actually quite useful. We propose to embed words and entity types into a lowdimensional vector space we train from annotated data produced by distant supervision thanks to Wikipedia. From this, we compute — offline — a feature vector representing each word. When used with a vanilla recurrent neural network model, this representation yields substantial improvements. We establish a new state-of-the-art F1 score of 87.95 on ONTONOTES 5.0, while matching state-of-the-art performance with a F1 score of 91.73 on the over-studied CONLL-2003 dataset.
A Closed-Form Solution to Natural Image Matting

Interactive digital matting, the process of extracting a foreground object from an image based on limited user input, is an important task in image and video editing. From a computer vision perspective, this task is extremely challenging because it is massively ill-posed - at each pixel we must estimate the foreground and the background colors, as well as the foreground opacity ("alpha matte") from a single color measurement. Current approaches either restrict the estimation to a small part of the image, estimating foreground and background colors based on nearby pixels where they are known, or perform iterative nonlinear estimation by alternating foreground and background color estimation with alpha estimation. In this paper, we present a closed-form solution to natural image matting. We derive a cost function from local smoothness assumptions on foreground and background colors and show that in the resulting expression, it is possible to analytically eliminate the foreground and background colors to obtain a quadratic cost function in alpha. This allows us to find the globally optimal alpha matte by solving a sparse linear system of equations. Furthermore, the closed-form formula allows us to predict the properties of the solution by analyzing the eigenvectors of a sparse matrix, closely related to matrices used in spectral image segmentation algorithms. We show that high-quality mattes for natural images may be obtained from a small amount of user input.
Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions

Emerging technologies such as the Internet of Things (IoT) require latency-aware computation for real-time application processing. In IoT environments, connected things generate a huge amount of data, which are generally referred to as big data. Data generated from IoT devices are generally processed in a cloud infrastructure because of the on-demand services and scalability features of the cloud computing paradigm. However, processing IoT application requests on the cloud exclusively is not an efficient solution for some IoT applications, especially time-sensitive ones. To address this issue, Fog computing, which resides in between cloud and IoT devices, was proposed. In general, in the Fog computing environment, IoT devices are connected to Fog devices. These Fog devices are located in close proximity to users and are responsible for intermediate computation and storage. One of the key challenges in running IoT applications in a Fog computing environment are resource allocation and task scheduling. Fog computing research is still in its infancy, and taxonomy-based investigation into the requirements of Fog infrastructure, platform, and applications mapped to current research is still required. This survey will help the industry and research community synthesize and identify the requirements for Fog computing. This paper starts with an overview of Fog computing in which the definition of Fog computing, research trends, and the technical differences between Fog and cloud are reviewed. Then, we investigate numerous proposed Fog computing architectures and describe the components of these architectures in detail. From this, the role of each component will be defined, which will help in the deployment of Fog computing. Next, a taxonomy of Fog computing is proposed by considering the requirements of the Fog computing paradigm. We also discuss existing research works and gaps in resource allocation and scheduling, fault tolerance, simulation tools, and Fog-based microservices. Finally, by addressing the limitations of current research works, we present some open issues, which will determine the future research direction for the Fog computing paradigm.
NLP Transformations Question Transducer Question Ranker Source Sentences Derived Sentences

We describe an extensible approach to generating questions for the purpose of reading comprehension assessment and practice. Our framework for question generation composes general-purpose rules to transform declarative sentences into questions, is modular in that existing NLP tools can be leveraged, and includes a statistical component for scoring questions based on features of the input, output, and transformations performed. In an evaluation in which humans rated questions according to several criteria, we found that our implementation achieves 43.3% precisionat-10 and generates approximately 6.8 acceptable questions per 250 words of source text.
Overview of the Author Identification Task at PAN-2018: Cross-domain Authorship Attribution and Style Change Detection

Author identification attempts to reveal the authors behind texts. It is an emerging area of research associated with applications in literary research, cyber-security, forensics, and social media analysis. In this edition of PAN, we study two task, the novel task of cross-domain authorship attribution, where the texts of known and unknown authorship belong to different domains, and style change detection, where single-author and multi-author texts are to be distinguished. For the former task, we make use of fanfiction texts, a large part of contemporary fiction written by non-professional authors who are inspired by specific well-known works, to enable us control the domain of texts for the first time. We describe a new corpus of fanfiction texts covering five languages (English, French, Italian, Polish, and Spanish). For the latter, a new data set of Q&As covering multiple topics in English is introduced. We received 11 submissions for the cross-domain authorship attribution task and 5 submissions for the style change detection task. A survey of participant methods and analytical evaluation results are presented in this paper.
Deep Face Recognition

The goal of this paper is face recognition – from either a single photograph or from a set of faces tracked in a video. Recent progress in this area has been due to two factors: (i) end to end learning for the task using a convolutional neural network (CNN), and (ii) the availability of very large scale training datasets. We make two contributions: first, we show how a very large scale dataset (2.6M images, over 2.6K people) can be assembled by a combination of automation and human in the loop, and discuss the trade off between data purity and time; second, we traverse through the complexities of deep network training and face recognition to present methods and procedures to achieve results comparable to the state of the art on the standard LFW and YTF face benchmarks.
Contributions to the study of SMS spam filtering: new collection and results

The growth of mobile phone users has lead to a dramatic increasing of SMS spam messages. In practice, fighting mobile phone spam is difficult by several factors, including the lower rate of SMS that has allowed many users and service providers to ignore the issue, and the limited availability of mobile phone spam-filtering software. On the other hand, in academic settings, a major handicap is the scarcity of public SMS spam datasets, that are sorely needed for validation and comparison of different classifiers. Moreover, as SMS messages are fairly short, content-based spam filters may have their performance degraded. In this paper, we offer a new real, public and non-encoded SMS spam collection that is the largest one as far as we know. Moreover, we compare the performance achieved by several established machine learning methods. The results indicate that Support Vector Machine outperforms other evaluated classifiers and, hence, it can be used as a good baseline for further comparison.
Scaling theory for quasibrittle structural failure.

This inaugural article has a twofold purpose: (i) to present a simpler and more general justification of the fundamental scaling laws of quasibrittle fracture, bridging the asymptotic behaviors of plasticity, linear elastic fracture mechanics, and Weibull statistical theory of brittle failure, and (ii) to give a broad but succinct overview of various applications and ramifications covering many fields, many kinds of quasibrittle materials, and many scales (from 10(-8) to 10(6) m). The justification rests on developing a method to combine dimensional analysis of cohesive fracture with second-order accurate asymptotic matching. This method exploits the recently established general asymptotic properties of the cohesive crack model and nonlocal Weibull statistical model. The key idea is to select the dimensionless variables in such a way that, in each asymptotic case, all of them vanish except one. The minimal nature of the hypotheses made explains the surprisingly broad applicability of the scaling laws.
Survey of Network on Chip (NoC) Architectures & Contributions

Multiprocessor architectures and platforms have been introduced to extend the applicability of Moore’s law. They depend on concurrency and synchronization in both software and hardware to enhance the design productivity and system performance. These platforms will also have to incorporate highly scalable, reusable, predictable, costand energy-efficient architectures. With the rapidly approaching billion transistors era, some of the main problems in deep sub-micron technologies which are characterized by gate lengths in the range of 60-90 nm, will arise from non-scalable wire delays, errors in signal integrity and unsynchronized communications. These problems may be overcome by the use of Network on Chip (NOC) architecture. In this paper, we have summarized over sixty research papers and contributions in NOC area.
Detection of Face Spoofing Using Visual Dynamics

Rendering a face recognition system robust is vital in order to safeguard it against spoof attacks carried out using printed pictures of a victim (also known as print attack) or a replayed video of the person (replay attack). A key property in distinguishing a live, valid access from printed media or replayed videos is by exploiting the information dynamics of the video content, such as blinking eyes, moving lips, and facial dynamics. We advance the state of the art in facial antispoofing by applying a recently developed algorithm called dynamic mode decomposition (DMD) as a general purpose, entirely data-driven approach to capture the above liveness cues. We propose a classification pipeline consisting of DMD, local binary patterns (LBPs), and support vector machines (SVMs) with a histogram intersection kernel. A unique property of DMD is its ability to conveniently represent the temporal information of the entire video as a single image with the same dimensions as those images contained in the video. The pipeline of DMD + LBP + SVM proves to be efficient, convenient to use, and effective. In fact only the spatial configuration for LBP needs to be tuned. The effectiveness of the methodology was demonstrated using three publicly available databases: (1) print-attack; (2) replay-attack; and (3) CASIA-FASD, attaining comparable results with the state of the art, following the respective published experimental protocols.
The effect of two plyometric training techniques on muscular power and agility in youth soccer players.

The aim of this study was to compare the effects of two plyometric training techniques on power and agility in youth soccer players. Twelve males from a semiprofessional football club's academy (age = 17.3 +/- 0.4 years, stature = 177.9 +/- 5.1 cm, mass = 68.7 +/- 5.6 kg) were randomly assigned to 6 weeks of depth jump (DJ) or countermovement jump (CMJ) training twice weekly. Participants in the DJ group performed drop jumps with instructions to minimize ground-contact time while maximizing height. Participants in the CMJ group performed jumps from a standing start position with instructions to gain maximum jump height. Posttraining, both groups experienced improvements in vertical jump height (p < 0.05) and agility time (p < 0.05) and no change in sprint performance (p > 0.05). There were no differences between the treatment groups (p > 0.05). The study concludes that both DJ and CMJ plyometrics are worthwhile training activities for improving power and agility in youth soccer players.
An Investigation Into Computer Forensic Tools

Cyber-crime has reached unprecedented proportions in this day and age. In addition, the internet has created a world with seemingly no barriers while making a countless number of tools available to the cyber-criminal. In light of this, Computer Forensic Specialists employ state-of-the-art tools and methodologies in the extraction and analysis of data from storage devices used at the digital crime scene. The focus of this paper is to conduct an investigation into some of these Forensic tools eg.Encase®. This investigation will address commonalities across the Forensic tools, their essential differences and ultimately point out what features need to be improved in these tools to allow for effective autopsies of storage devices.
Corrections to 'Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects'

ÐThis paper presents a new variational framework for detecting and tracking multiple moving objects in image sequences. Motion detection is performed using a statistical framework for which the observed interframe difference density function is approximated using a mixture model. This model is composed of two components, namely, the static (background) and the mobile (moving objects) one. Both components are zero-mean and obey Laplacian or Gaussian law. This statistical framework is used to provide the motion detection boundaries. Additionally, the original frame is used to provide the moving object boundaries. Then, the detection and the tracking problem are addressed in a common framework that employs a geodesic active contour objective function. This function is minimized using a gradient descent method, where a flow deforms the initial curve towards the minimum of the objective function, under the influence of internal and external image dependent forces. Using the level set formulation scheme, complex curves can be detected and tracked while topological changes for the evolving curves are naturally managed. To reduce the computational cost required by a direct implementation of the level set formulation scheme, a new approach named Hermes is proposed. Hermes exploits aspects from the well-known front propagation algorithms (Narrow Band, Fast Marching) and compares favorably to them. Very promising experimental results are provided using real video sequences. Index TermsÐFront propagation, geodesic active contours, level set theory, motion detection, tracking.
Human immunodeficiency virus/acquired immune deficiency syndrome: A survey on the knowledge, attitude, and practice among medical professionals at a tertiary health-care institution in Uttarakhand, India

Health-care professionals are trained health-care providers who occupy a potential vanguard position in human immunodeficiency virus (HIV)/acquired immune deficiency syndrome (AIDS) prevention programs and the management of AIDS patients. This study was performed to assess HIV/AIDS-related knowledge, attitude, and practice (KAP) and perceptions among health-care professionals at a tertiary health-care institution in Uttarakhand, India, and to identify the target group where more education on HIV is needed.



A cross-sectional KAP survey was conducted among five groups comprising consultants, residents, medical students, laboratory technicians, and nurses. Probability proportional to size sampling was used for generating random samples. Data analysis was performed using charts and tables in Microsoft Excel 2016, and statistical analysis was performed using the Statistical Package for the Social Science software version 20.0.



Most participants had incomplete knowledge regarding the various aspects of HIV/AIDS. Attitude in all the study groups was receptive toward people living with HIV/AIDS. Practical application of knowledge was best observed in the clinicians as well as medical students. Poor performance by technicians and nurses was observed in prevention and prophylaxis. All groups were well informed about the National AIDS Control Policy except technicians.



Poor knowledge about HIV infection, particularly among the young medical students and paramedics, is evidence of the lacunae in the teaching system, which must be kept in mind while formulating teaching programs. As suggested by the respondents, Information Education Communication activities should be improvised making use of print, electronic, and social media along with interactive awareness sessions, regular continuing medical educations, and seminars to ensure good quality of safe modern medical care.