nils-herrmann/clean_scirepeval huggingface dataset:
Exercise prescription for overhead athletes with shoulder pathology: a systematic review with best evidence synthesis.

To produce a best evidence synthesis of exercise prescription used when treating shoulder pathology in the overhead athlete.



A systematic review of exercises used in overhead athletes including case studies and clinical commentaries.



MEDLINE, PubMed, SPORTDiscus and CINAHL from database inception through July 8, 2016.



We examined data from randomised controlled trials and prospective cohort (level I-IV evidence) studies that addressed exercise intervention in the rehabilitation of the overhead athlete with shoulder pathology. Case studies and clinical commentaries (level V evidence) were examined to account for expert opinion-based research. Data were combined using best evidence synthesis and graded (A-F) recommendations (Centre for Evidence-Based Medicine).



There were 33 unique exercises in six level I-IV studies that met our inclusion criteria. Most exercises were single-plane, upper extremity exercises performed below 90o of elevation. There were 102 unique exercises in 33 level V studies that met our inclusion criteria. These exercises emphasised plyometrics, kinetic chain and sport-specific training.



Overall, evidence for exercise interventions in overhead athletes with shoulder pathology is dominated by expert opinion (grade D). There is great variability between exercise approaches suggested by experts and those investigated in research studies and the overall level of evidence is low. The strongest available evidence (level B) supports the use of single-plane, open chain upper extremity exercises performed below 90° of elevation and closed chain upper extremity exercises. Clinical expert pieces support a more advanced, global treatment approach consistent with the complex, multidimensional nature of sport.
Mobile commerce adoption in China and the United States: a cross-cultural study

Mobile communication technologies have penetrated consumer markets throughout the world. Mobile commerce is likely to make a strong influence on business activities, consumer behavior, as well as national and global markets. Thus the identification of factors that influence mobile commerce adoption has significant value. In a global context, this study identified nine factors affecting mobile commerce adoption based on published research in MIS. These factors were investigated in China and the United States, and a comparative examination was conducted. A survey was conducted on 190 individual mobile commerce users in China and USA. Results show that there are several significant differences among the antecedents and their impacts on consumer intention to use mobile commerce in the two cultural settings. The study provides a number of practical insights and informs vendors seeking to enter the Chinese and the US marketplace with specific information on user perspectives.
VQM-based QoS/QoE mapping for streaming video

Numerous new broadband and multimedia applications have emerged in recent years, in which streaming video services are particularly of interest. Unlike data communication, streaming video requires a subjective measure of quality referred to as the Quality of Experience (QoE), but the subjective evaluation is very time consuming and complex. We are proposing an objective approach to provide mapping between QoE and Quality of Service (QoS), in which Video Quality Metric (VQM) is utilized to indicate QoE. We have analyzed the emulation results and derived a simple formula to estimate QoE from QoS parameters for streaming video under certain conditions. This method is non-intrusive and quick to use.
Long-Span Program Behavior Modeling and Attack Detection

Intertwined developments between program attacks and defenses witness the evolution of program anomaly detection methods. Emerging categories of program attacks, e.g., non-control data attacks and data-oriented programming, are able to comply with normal trace patterns at local views. This article points out the deficiency of existing program anomaly detection models against new attacks and presents long-span behavior anomaly detection (LAD), a model based on mildly context-sensitive grammar verification. The key feature of LAD is its reasoning of correlations among arbitrary events that occurred in long program traces. It extends existing correlation analysis between events at a stack snapshot, e.g., paired call and ret, to correlation analysis among events that historically occurred during the execution. The proposed method leverages specialized machine learning techniques to probe normal program behavior boundaries in vast high-dimensional detection space. Its two-stage modeling/detection design analyzes event correlation at both binary and quantitative levels. Our prototype successfully detects all reproduced real-world attacks against sshd, libpcre, and sendmail. The detection procedure incurs 0.1 ms to 1.3 ms overhead to profile and analyze a single behavior instance that consists of tens of thousands of function call or system call events.
Design and Control of Ball Wheel Omnidirectional Vehicles

A new class cif ball wheel mechanisms f o r omnidirectional vehicles is presented. Th i s ball wheel mechanism can be designed to yield ful ly mobile vehicles that are no t only free of any k inemat ic singularity but are configuration-,invaraant in k inemat ic behavior. Invariant k inemat ics greatly simplifies the control of smooth and precise vehicle motion. Multiple displacement sensors are easily incorporated in to each ball wheel mechanism. Th i s unique feature enables the detection of slip between tzres and the f loor and also indicates at which tire the slip occurs. Th i s allows traction control to be i m plemented to ful ly exploit the available floor fr ic t ion while accurate dead reckoning navigation continues f r o m two nonslipping tires. A prototype vehicle wi th three ball wheel mechanisms is implemented. Smooth mo t ion and precise dead reckoning are accomplished.
Scaling Spark in the Real World: Performance and Usability

Apache Spark is one of the most widely used open source processing engines for big data, with rich language-integrated APIs and a wide range of libraries. Over the past two years, our group has worked to deploy Spark to a wide range of organizations through consulting relationships as well as our hosted service, Databricks. We describe the main challenges and requirements that appeared in taking Spark to a wide set of users, and usability and performance improvements we have made to the engine in response.
Data Storage Management in Cloud Environments: Taxonomy, Survey, and Future Directions

Storage as a Service (StaaS) is a vital component of cloud computing by offering the vision of a virtually infinite pool of storage resources. It supports a variety of cloud-based data store classes in terms of availability, scalability, ACID (Atomicity, Consistency, Isolation, Durability) properties, data models, and price options. Application providers deploy these storage classes across different cloud-based data stores not only to tackle the challenges arising from reliance on a single cloud-based data store but also to obtain higher availability, lower response time, and more cost efficiency. Hence, in this article, we first discuss the key advantages and challenges of data-intensive applications deployed within and across cloud-based data stores. Then, we provide a comprehensive taxonomy that covers key aspects of cloud-based data store: data model, data dispersion, data consistency, data transaction service, and data management cost. Finally, we map various cloud-based data stores projects to our proposed taxonomy to validate the taxonomy and identify areas for future research.
The Genetic Structure of Marijuana and Hemp

Despite its cultivation as a source of food, fibre and medicine, and its global status as the most used illicit drug, the genus Cannabis has an inconclusive taxonomic organization and evolutionary history. Drug types of Cannabis (marijuana), which contain high amounts of the psychoactive cannabinoid Δ9-tetrahydrocannabinol (THC), are used for medical purposes and as a recreational drug. Hemp types are grown for the production of seed and fibre, and contain low amounts of THC. Two species or gene pools (C. sativa and C. indica) are widely used in describing the pedigree or appearance of cultivated Cannabis plants. Using 14,031 single-nucleotide polymorphisms (SNPs) genotyped in 81 marijuana and 43 hemp samples, we show that marijuana and hemp are significantly differentiated at a genome-wide level, demonstrating that the distinction between these populations is not limited to genes underlying THC production. We find a moderate correlation between the genetic structure of marijuana strains and their reported C. sativa and C. indica ancestry and show that marijuana strain names often do not reflect a meaningful genetic identity. We also provide evidence that hemp is genetically more similar to C. indica type marijuana than to C. sativa strains.
Students’ opinions on blended learning and its implementation in terms of their learning styles

The purpose of this article is to examine students’ views on the blended learning method and its use in relation to the students’ individual learning style. The study was conducted with 31 senior students. Web based media together with face to face classroom settings were used in the blended learning framework. A scale of Students’ Views on Blended Learning and its implementation, Kolb’s Learning Style Inventory, Pre-Information Form and open ended questions were used to gather data. The majority of the students’ fell into assimilators, accommodators and convergers learning styles. Results revealed that students’ views on blended learning method and its use are quite positive.
Argumentation Mining: Where are we now, where do we want to be and how do we get there?

This paper gives a short overview of the state-of-the-art and goals of argumentation mining and it provides ideas for further research. Its content is based on two invited lectures on argumentation mining respectively at the FIRE 2013 conference at the India International Center in New Delhi, India and a lecture given as SICSA distinguished visitor at the University of Dundee, UK in the summer of 2014.
Virtual Hagia Sophia : Restitution , Visualization and Virtual Life Simulation

This paper presents a case study centered on the virtual restitution and virtual life simulation of a highly complex and endangered heritage edifice: the church of Hagia Sophia, in Istanbul, Turkey (added in 1996 in the annual list of endangered monuments by the World Monuments Watch). The goal of this article is to describe the techniques used in order to achieve a photo-realistic simulation of the selected space and its characters, as well as to point out the challenges and solutions that such a work implies at different stages in production. Most of these issues are mainly focused on the reconstruction of the architecture of the site, but in order to achieve an accurate simulation the social aspect must not be forgotten. The importance of a heritage site resides as well in the historical characters and the social interactions that were taking place there: this information allows a better understanding of the function and the importance of the selected site in connection with the cultural aspects of the life at a certain time. In order to strengthen the feeling of immersion in a heritage edifice virtually restituted, it is important to recreate virtual life and describe the timely evolutionary aspects of the edifice as well.
A Theory of Social Comparison Processes

In this paper we shall present a further development of a previously published theory concerning opinion influence processes in social groups (7). This further development has enabled us to extend the theory to deal with other areas, in addition to opinion formation, in which social comparison is important. Specifically, we shall develop below how the theory applies to the appraisal and evaluation of abilities as well as opinions. Such theories and hypotheses in the area of social psychology are frequently viewed in terms of how “plausible” they seem. “Plausibility” usually means whether or not the theory or hypothesis fits one’s intuition or one’s common sense. In this meaning much of the theory which is to be presented here is not” plausible “. The theory does, however, explain a considerable amount of data and leads to testable derivations. Three experiments, specifically designed to test predictions from this extension of the theory, have now been completed (5, 12, 19). They all provide good corroboration. We will in the following pages develop the theory and present the relevant data.
Extending web applications with a lightweight zero knowledge proof authentication

User authentication is a crucial requirement for secure transactions and access to the sensitive resources on the Web. We propose, implement and evaluate a Zero-Knowledge Proof Authentication (ZKP) algorithm based on isomorphic graphs. The proposed mechanism allows for authentication with varying confidence and security levels.
 We suggest that most of the computations should be carried out by the user's web browser without revealing password or login at any point in time; instead generated random isomorphic graphs and permutation functions based on the user login/password can be exchanged.
 Our experimental evaluation shows that by combining the asynchronous web with ZKP protocols, it is feasible to satisfy existing usability standards on the web.
Modulation of Gut Microbiota in the Management of Metabolic Disorders: The Prospects and Challenges

The gut microbiota plays a number of important roles including digestion, metabolism, extraction of nutrients, synthesis of vitamins, prevention against pathogen colonization, and modulation of the immune system. Alterations or changes in composition and biodiversity of the gut microbiota have been associated with many gastrointestinal tract (GIT) disorders such as inflammatory bowel disease and colon cancer. Recent evidence suggests that altered composition and diversity of gut microbiota may play a role in the increased prevalence of metabolic diseases. This review article has two main objectives. First, it underscores approaches (such as probiotics, prebiotics, antimicrobial agents, bariatric surgery, and weight loss strategies) and their prospects in modulating the gut microbiota in the management of metabolic diseases. Second, it highlights some of the current challenges and discusses areas of future research as it relates to the gut microbiota and metabolic diseases. The prospect of modulating the gut microbiota seems promising. However, considering that research investigating the role of gut microbiota in metabolic diseases is still in its infancy, more rigorous and well-designed in vitro, animal and clinical studies are needed.
Approximate Primal Solutions and Rate Analysis for Dual Subgradient Methods

We study primal solutions obtained as a by-product of subgradient methods when solving the Lagrangian dual of a primal convex constrained optimization problem (possibly nonsmooth). The existing literature on the use of subgradient methods for generating primal optimal solutions is limited to the methods producing such solutions only asymptotically (i.e., in the limit as the number of subgradient iterations increases to infinity). Furthermore, no convergence rate results are known for these algorithms. In this paper, we propose and analyze dual subgradient methods using averaging to generate approximate primal optimal solutions. These algorithms use a constant stepsize as opposed to a diminishing stepsize which is dominantly used in the existing primal recovery schemes. We provide estimates on the convergence rate of the primal sequences. In particular, we provide bounds on the amount of feasibility violation of the generated approximate primal solutions. We also provide upper and lower bounds on the primal function values at the approximate solutions. The feasibility violation and primal value estimates are given per iteration, thus providing practical stopping criteria. Our analysis relies on the Slater condition and the inherited boundedness properties of the dual problem under this condition.
Partial entity structure: a compact non-manifold boundary representation based on partial topological entities

Non-manifold boundary representations have gained a great deal of popularity in recent years and various representation schemes have been proposed because they allow an even wider range of objects for various applications than conventional manifold representations. However, since these schemes are mainly interested in describing sufficient adjacency relationships of topological entities, the models represented in these schemes occupy too much storage space redundantly although they are very efficient in answering queries on topological adjacency relationships. Storage requirement can arise as a crucial problem in models in which topological data is more dominant than geometric data, such as tessellated or mesh models.
To solve this problem, in this paper, we propose a compact non-manifold boundary representation, called the partial entity structure, which allows the reduction of the storage size to half that of the radial edge structure, which is known as a time efficient non-manifold data structure, while allowing full topological adjacency relationships to be derived without loss of efficiency. This representation contains not only the conventional primitive entities like the region, face, edge, and vertex, but also the partial topological entities such as the partial-face, partial-edge, and partial-vertex for describing non-manifold conditions at vertices, edges, and faces. In order to verify the time and storage efficiency of the partial entity structure, the time complexity of basic query procedures and the storage requirement for typical geometric models are derived and compared with those of existing schemes. Furthermore, a set of the generalized Euler operators and typical high-level modeling capabilities such as Boolean operations are also implemented to confirm that our data structure is sound and easy to be manipulated.
Nonlinear model predictive control for aerial manipulation

This paper presents a nonlinear model predictive controller to follow desired 3D trajectories with the end effector of an unmanned aerial manipulator (i.e., a multirotor with a serial arm attached). To the knowledge of the authors, this is the first time that such controller runs online and on board a limited computational unit to drive a kinematically augmented aerial vehicle. Besides the trajectory following target, we explore the possibility of accomplishing other tasks during flight by taking advantage of the system redundancy. We define several tasks designed for aerial manipulators and show in simulation case studies how they can be achieved by either a weighting strategy, within a main optimization process, or a hierarchical approach consisting on nested optimizations. Moreover, experiments are presented to demonstrate the performance of such controller in a real robot.
YASA: Yet Another Time Series Segmentation Algorithm for Anomaly Detection in Big Data Problems

Time series patterns analysis had recently attracted the attention of the research community for real-world applications. Petroleum industry is one of the application contexts where these problems are present, for instance for anomaly detection. Offshore petroleum platforms rely on heavy turbomachines for its extraction, pumping and generation operations. Frequently, these machines are intensively monitored by hundreds of sensors each, which send measurements with a high frequency to a concentration hub. Handling these data calls for a holistic approach, as sensor data is frequently noisy, unreliable, inconsistent with a priori problem axioms, and of a massive amount. For the anomalies detection problems in turbomachinery, it is essential to segment the dataset available in order to automatically discover the operational regime of the machine in the recent past. In this paper we propose a novel time series segmentation algorithm adaptable to big data problems and that is capable of handling the high volume of data involved in problem contexts. As part of the paper we describe our proposal, analyzing its computational complexity. We also perform empirical studies comparing our algorithm with similar approaches when applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.
Investigating students' perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system

Although the benefits of e-learning have been discussed in various previous studies; it is a critical issue of better understanding the reasons why some learners are dissatisfied with the e-learning experience. Therefore, this research investigates learners’ satisfaction, behavioral intentions, and the effectiveness of the Blackboard e-learning system. A total of 424 university students were surveyed using a standard questionnaire. The results showed that perceived self-efficacy is a critical factor that influences learners’ satisfaction with the Blackboard e-learning system. Perceived usefulness and perceived satisfaction both contribute to the learners’ behavioral intention to use the e-learning system. Furthermore, e-learning effectiveness can be influenced by multimedia instruction, interactive learning activities, and e-learning system quality. This research proposes a conceptual model for understanding learners’ satisfaction, behavioral intention, and effectiveness of using the e-learning system.
Cascaded Feature Network for Semantic Segmentation of RGB-D Images

Fully convolutional network (FCN) has been successfully applied in semantic segmentation of scenes represented with RGB images. Images augmented with depth channel provide more understanding of the geometric information of the scene in the image. The question is how to best exploit this additional information to improve the segmentation performance.,,In this paper, we present a neural network with multiple branches for segmenting RGB-D images. Our approach is to use the available depth to split the image into layers with common visual characteristic of objects/scenes, or common “scene-resolution”. We introduce context-aware receptive field (CaRF) which provides a better control on the relevant contextual information of the learned features. Equipped with CaRF, each branch of the network semantically segments relevant similar scene-resolution, leading to a more focused domain which is easier to learn. Furthermore, our network is cascaded with features from one branch augmenting the features of adjacent branch. We show that such cascading of features enriches the contextual information of each branch and enhances the overall performance. The accuracy that our network achieves outperforms the stateof-the-art methods on two public datasets.