nils-herrmann/clean_scirepeval huggingface dataset:
Factors determining students' low usage of mobile tools in their English vocabulary learning

espanolEl articulo se basa en los resultados de un estudio a gran escala con metodos mixtos que tuvo lugar en Rumania en 2014. El estudio exploro la utilizacion de estrategias digitales para el aprendizaje de vocabulario en lengua inglesa por parte de estudiantes de secundaria y las actitudes de los estudiantes rumanos hacia el aprendizaje del vocabulario de un segundo idioma con herramientas digitales. Los resultados del estudio principal indican que los participantes tenian un bajo uso de herramientas y estrategias digitales en su aprendizaje de vocabulario y que la mayoria de los participantes mostraron actitudes neutrales hacia el uso de herramientas moviles (MALL) en su aprendizaje del vocabulario. Los datos tambien revelaron que ciertos factores determinan el poco uso de los alumnos de las herramientas digitales en la adquisicion de un segundo idioma en relacion al aprendizaje del vocabulario. Estos factores son: la falta de una "cultura" asociada con el uso de herramientas digitales en la clase de lengua, el estilo personal de aprendizaje, algunas cuestiones pedagogicas asociadas a las herramientas digitales, la falta de la guia del profesor, la percepcion de las herramientas digitales como meras herramientas de entretenimiento, un comportamiento multitarea de los estudiantes en relacion con el uso de su dispositivo, una preferencia por el aprendizaje inconsciente del vocabulario y el nivel de motivacion para utilizar la tecnologia para la adquisicion de un segundo idioma. EnglishThe article is based on the results of a large scale mixed methods study which took place in Romania in 2014. The study explored high school students' use of digital vocabulary English learning strategies and the attitudes of Romanian students towards learning vocabulary in second language acquisition with digital tools. The results of the main study indicated that the participants had a low usage of digital tools and strategies in their vocabulary learning and that the majority of the participants showed neutral attitudes towards the use of mobile assisted learning (MALL) tools in their vocabulary learning. The data also revealed that certain factors determine students' low usage of digital tools in second language acquisition in their vocabulary learning. These factors were: the lack of a 'culture' associated with the use of digital tools in the language classroom, personal learning style, some pedagogical issues associated with digital tools, lack of teacher guidance, the view of digital tools as simply entertaining tools, students' multitasking behavior in association with the use of their device, a preference for unconscious learning of vocabulary and the level of motivation to use technology for second language acquisition.
Training Learners to Use Quizlet Vocabulary Activities on Mobile Phones in Vietnam with Facebook.

University of Foreign Language Studies, University of Danang phuongtran@ufl.udn.vn Mobile phone ownership among university students in Vietnam has reached almost 100% exceeding that of Internet capable desktop computers. This has made them increasingly popular to allow learners to carry out learning activities outside of the classroom, but some studies have suggested that learners are not always willing to engage in activities outside of the classroom (Kim et al. 2013) Recent research has suggested that providing training to learners that includes not only how but also why activities are important can improve learner engagement in mobile based activities (Stockwell Hubbard, 2014) In this presentation, Vietnamese learners of English engaged in vocabulary and grammar tasks using the Quizlet app on their mobile phones outside of class time. Learners were provided with technical training in class, while ongoing strategic and pedagogical training were provided through a combination of in class activities and interactions through a dedicated Facebook page over a 5 week period. Usage patterns of the site were recorded through a learning journal and interactions on the Facebook page were analysed to determine the nature of the discussions that took place. Learner attitudes towards the tasks and the training were examined through preand post questionnaires and a focus group discussion. The results are discussed in terms of the problems encountered, and some suggestions for providing appropriate training to learning through mobile phones outside of class through social networking.
Frontostriatal White Matter Integrity Predicts Development of Delay of Gratification: A Longitudinal Study
Venue: The Journal of Neuroscience

The ability to delay gratification increases considerably across development. Here, we test the hypothesis that this impulse control capacity is driven by increased maturation of frontostriatal circuitry using a fiber tracking approach combined with longitudinal imaging. In total, 192 healthy volunteers between 8 and 26 years underwent diffusion tensor imaging scanning and completed a delay discounting task twice, separated by a 2 year interval. We investigated dynamic associations between frontostriatal white matter (WM) integrity and delay of gratification skills. Moreover, we examined the predictive value of frontostriatal WM integrity for future delay of gratification skills. Results showed that delay discounting increases with age in a quadratic fashion, with greatest patience during late adolescence. Data also indicated nonlinear development of frontostriatal WM, with relative fast development during childhood and early adulthood and on average little change during mid adolescence. Furthermore, the positive association between age and delay discounting was further increased in individuals with higher WM integrity of the frontostriatal tracts. Predictive analysis showed that frontostriatal WM development explained unique variance in current and future delay of gratification skills. This study adds to a descriptive relation between WM integrity and delay of gratification by showing that maturation of frontostriatal connectivity predicts changes in delay of gratification skills. These findings have implications for studies examining deviances in impulse control by showing that the developmental path between striatum and prefrontal cortex may be an important predictor for when development goes astray.  During the transition from childhood to adulthood, individuals generally show increased patience and become better in delaying gratification. The exact neural correlates of delay of gratification, however, remain poorly understood. By measuring both frontostriatal white matter (WM) integrity and delay of gratification skills at two time points, we were able to provide links for our understanding of the neural mechanisms underlying this type of impulse regulation capacity. We demonstrate that the ability to delay gratification improves between childhood and young adulthood and this improvement is predicted by the integrity of frontostriatal WM connections. This study adds to a descriptive relation between WM quality and delay of gratification by showing that maturation of frontostriatal connectivity predicts improvements in delay of gratification skills.
Using Field Experiments in Accounting and Finance

The gold standard in the sciences is uncovering causal relationships. A growing literature in economics utilizes field experiments as a methodology to establish causality between variables. Taking lessons from the economics literature, this study provides an "A to Z" description of how to conduct field experiments in accounting and finance. We begin by providing a user's guide into what a field experiment is, what behavioral parameters field experiments identify, and how to efficiently generate and analyze experimental data. We then provide a discussion of extant field experiments that touch on important issues in accounting and finance, and we also review areas that have ample opportunities for future field experimental explorations. We conclude that the time is ripe for field experimentation to deepen our understanding of important issues in accounting and finance.
Intellectual capital and business performance: An exploratory study of the impact of cloud based accounting and finance infrastructure

The purpose of this paper, building on previous studies of intellectual capital (IC) and business performance, is an exploratory study of how the use of cloud based accounting/finance infrastructure affects the business performance of small and medium sized enterprises (SMEs) The paper aims to discuss these issues.  A survey method is used to capture perceptions of how cloud based accounting/finance infrastructure affects business performance in SMEs. The study assumes that although accounting/finance systems are generally regarded as one element of a firm's structural capital; the introduction of a cloud based infrastructure in the accounting/finance area has the potential to positively impact on all three elements of a firm's IC. Based on the survey data collected, a conceptual model was formulated to test the relationship between cloud based accounting/finance infrastructure and business performance through the prism of firms' IC.  The results indicate
DRE ip: A Verifiable E Voting Scheme Without Tallying Authorities
Venue: ESORICS

Nearly all verifiable e voting schemes require trustworthy authorities to perform the tallying operations. An exception is the DRE i system which removes this requirement by pre computing all encrypted ballots before the election using random factors that will later cancel out and allow the public to verify the tally after the election. While the removal of tallying authorities significantly simplifies election management, the pre computation of ballots necessitates secure ballot storage, as leakage of precomputed ballots endangers voter privacy. In this paper, we address this problem and propose DRE ip (DRE i with enhanced privacy) Adopting a different design strategy, DRE ip is able to encrypt ballots in real time in such a way that the election tally can be publicly verified without decrypting the cast ballots. As a result, DRE ip achieves end to end verifiability without tallying authorities, similar to DRE i, but with a significantly stronger guarantee on voter privacy. In the event that the voting machine is fully compromised, the assurance on tallying integrity remains intact and the information leakage is limited to the minimum: only the partial tally at the time of compromise is leaked.
Review of optical flow technique for moving object detection
Venue: 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)

Object detection in a video is a challenging task in the field of image processing. Some applications of the domain are Human Machine Interaction (HMI) Security and Surveillance, Supplemented Authenticity, Traffic Monitoring on Roads, Medicinal Imaging etc. There happens to be a number of methods available for object detection. Each of the method has some constraints on the kind of application it has been used for. This paper presents one of such method which is termed as Optical Flow technique. This technique is found to be more robust and efficient for moving object detection and the same has been shown by an experiment in the paper. Applying optical flow to an image gives flow vectors of the points corresponding to the moving objects. Next part of marking the required moving object of interest counts to the post processing. Post processing is the legitimate contribution of the paper for moving object detection problem. This here is discussed as Blob Analysis. It is tested on datasets available online, real time videos and also on videos recorded manually. The results show that the moving objects are successfully detected using optical flow technique and the required post processing.
Human intersegmental reflexes from intercostal afferents to scalene muscles
Venue: Experimental physiology

What is the central question of this study? The aim was to determine whether specific reflex connections operate between intercostal afferents and the scalene muscles in humans, and whether these connections operate after a clinically complete cervical spinal cord injury. What is the main finding and its importance? This is the first description of a short latency inhibitory reflex connection between intercostal afferents from intercostal spaces to the scalene muscles in able bodied participants. We suggest that this reflex is mediated by large diameter afferents. This intercostal to scalene inhibitory reflex is absent after cervical spinal cord injury and may provide a way to monitor the progress of the injury.
Network Volume Anomaly Detection and Identification in Large Scale Networks Based on Online Time Structured Traffic Tensor Tracking
Venue: IEEE Transactions on Network and Service Management

This paper addresses network anomography, that is, the problem of inferring network level anomalies from indirect link measurements. This problem is cast as a low rank subspace tracking problem for normal flows under incomplete observations and an outlier detection problem for abnormal flows. Since traffic data is large scale time structured data accompanied with noise and outliers under partial observations, an efficient modeling method is essential. To this end, this paper proposes an online subspace tracking of a Hankelized time structured traffic tensor for normal flows based on the Candecomp/PARAFAC decomposition exploiting the recursive least squares algorithm. We estimate abnormal flows as outlier sparse flows via sparsity maximization in the underlying under constrained linear inverse problem. A major advantage is that our algorithm estimates normal flows by low dimensional matrices with time directional features as well as the spatial correlation of multiple links without using the past observed measurements and the past model parameters. Extensive numerical evaluations show that the proposed algorithm achieves faster convergence per iteration of model approximation and better volume anomaly detection performance compared to state of the art algorithms.
A Distributed Optimization Framework for Localization and Formation Control: Applications to Vision Based Measurements
Venue: IEEE Control Systems

Multiagent systems have been a major area of research for the last 15 years. This interest has been motivated by tasks that can be executed more rapidly in a collaborative manner or that are nearly impossible to carry out otherwise. To be effective, the agents need to have the notion of a common goal shared by the entire network (for instance, a desired formation) and individual control laws to realize the goal. The common goal is typically centralized, in the sense that it involves the state of all the agents at the same time. On the other hand, it is often desirable to have individual control laws that are distributed, in the sense that the desired action of an agent depends only on the measurements and states available at the node and at a small number of neighbors. This is an attractive quality because it implies an overall system that is modular and intrinsically more robust to communication delays and node failures.
Differential GNSS and Vision Based Tracking to Improve Navigation Performance in Cooperative Multi UAV Systems
Venue: Sensors

Autonomous navigation of micro UAVs is typically based on the integration of low cost Global Navigation Satellite System (GNSS) receivers and Micro Electro Mechanical Systems (MEMS) based inertial and magnetic sensors to stabilize and control the flight. The resulting navigation performance in terms of position and attitude accuracy may not suffice for other mission needs, such as the ones relevant to fine sensor pointing. In this framework, this paper presents a cooperative UAV navigation algorithm that allows a chief vehicle, equipped with inertial and magnetic sensors, a Global Positioning System (GPS) receiver, and a vision system, to improve its navigation performance (in real time or in the post processing phase) exploiting formation flying deputy vehicles equipped with GPS receivers. The focus is set on outdoor environments and the key concept is to exploit differential GPS among vehicles and vision based tracking (DGPS/Vision) to build a virtual additional navigation sensor whose information is then integrated in a sensor fusion algorithm based on an Extended Kalman Filter. The developed concept and processing architecture are described, with a focus on DGPS/Vision attitude determination algorithm. Performance assessment is carried out on the basis of both numerical simulations and flight tests. In the latter ones, navigation estimates derived from the DGPS/Vision approach are compared with those provided by the onboard autopilot system of a customized quadrotor. The analysis shows the potential of the developed approach, mainly deriving from the possibility to exploit magnetic and inertial independent accurate attitude information.
CNUSVM: Hybrid CNN Uneven SVM Model for Imbalanced Visual Learning
Venue: 2016 IEEE Second International Conference on Multimedia Big Data (BigMM)

Recently, deep Convolutional Neural Networks (CNNs) have been used to achieve state of the art performance on a wide range of visual learning tasks. However, when facing some imbalanced learning tasks where the training samples are unevenly distributed among different classes, CNNs tend to produce performance bias toward the majority class, making them not suitable for applications in which the recognition ability on the minority class is highly valued. To address the problem, this paper proposes a hybrid classification model by combining CNN with Support Vector Machine (SVM) that has uneven margins. In this model, CNN works as a feature extractor and the extracted features are then sent into a L2 SVM with linear uneven margins. We also develop a gradient descent learning approach for this hybrid CNN uneven SVM (CNUSVM) model by minimizing an uneven margin based L2 hinge loss. Our experiments on two benchmark datasets show that the CNUSVM model can make more favorable decisions for imbalanced visual learning tasks in comparison with the standard CNN and the hybrid CNN SVM model.
CDC Guideline for Prescribing Opioids for Chronic Pain United States, 2016.
Venue: JAMA

IMPORTANCE Primary care clinicians find managing chronic pain challenging. Evidence of long term efficacy of opioids for chronic pain is limited. Opioid use is associated with serious risks, including opioid use disorder and overdose.  To provide recommendations about opioid prescribing for primary care clinicians treating adult patients with chronic pain outside of active cancer treatment, palliative care, and end of life care. PROCESS The Centers for Disease Control and Prevention (CDC) updated a 2014 systematic review on effectiveness and risks of opioids and conducted a supplemental review on benefits and harms, values and preferences, and costs. CDC used the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) framework to assess evidence type and determine the recommendation category. EVIDENCE SYNTHESIS Evidence consisted of observational studies or randomized clinical trials with notable limitations, characterized as low quality using GRADE methodology. Meta analysis was not attempted due to the limited number of studies, variability in study designs and clinical heterogeneity, and methodological shortcomings of studies. No study evaluated long term =1 year) benefit of opioids for chronic pain. Opioids were associated with increased risks, including opioid use disorder, overdose, and death, with dose dependent effects. RECOMMENDATIONS There are 12 recommendations. Of primary importance, nonopioid therapy is preferred for treatment of chronic pain. Opioids should be used only when benefits for pain and function are expected to outweigh risks. Before starting opioids, clinicians should establish treatment goals with patients and consider how opioids will be discontinued if benefits do not outweigh risks. When opioids are used, clinicians should prescribe the lowest effective dosage, carefully reassess benefits and risks when considering increasing dosage to 50 morphine milligram equivalents or more per day, and avoid concurrent opioids and benzodiazepines whenever possible. Clinicians should evaluate benefits and harms of continued opioid therapy with patients every 3 months or more frequently and review prescription drug monitoring program data, when available, for high risk combinations or dosages. For patients with opioid use disorder, clinicians should offer or arrange evidence based treatment, such as medication assisted treatment with buprenorphine or methadone.  The guideline is intended to improve communication about benefits and risks of opioids for chronic pain, improve safety and effectiveness of pain treatment, and reduce risks associated with long term opioid therapy.
Legal regimes surrounding naloxone access: considerations for prescribers
Venue: The American journal of drug and alcohol abuse

Since the late 1980s, opioid related morbidity and mortality in the United States has dramatically increased. This serious epidemic requires a coordinated medical, public policy, and social response. It is becoming readily apparent that widespread provision of naloxone may help to address this problem. However, because naloxone access laws vary between states, the extent of antidote dissemination may be limited by a given provider's geographic location.  This review targets a physician or prescriber audience, in hopes of providing evidence for the safety and utility of naloxone, education on the baseline legal liability of naloxone provision and protections afforded by access laws, and resources for the proposal of statewide legislation to promote antidote distribution.  Evidence suggests that naloxone administration by laypersons, pursuant to physician prescription or standing order, is safe and effective for reversal of opioid overdose. As of July 2015, 44 states and the District of Columbia have passed naloxone access laws, offering varying degrees of protections for prescribers. Although the likelihood of naloxone related legal action may parallel that inherent to the usual practice of medicine, providers should be mindful of potential scenarios, exercise methods to mitigate risk, and appreciate the utility of comprehensive naloxone access legislation in orchestrating a coordinated response to the opioid overdose epidemic.
Compensated Convexity Methods for Approximations and Interpolations of Sampled Functions in Euclidean Spaces: Theoretical Foundations
Venue: SIAM J. Math. Anal.

We introduce Lipschitz continuous and $C^{1,1} geometric approximation and interpolation methods for sampled bounded uniformly continuous functions over compact sets and over complements of bounded open sets in \mathbb{R}^n$ by using compensated convex transforms. Error estimates are provided for the approximations of bounded uniformly continuous functions, of Lipschitz functions, and of $C^{1,1} functions. We also prove that our approximation methods, which are differentiation and integration free and not sensitive to sample type, are stable with respect to the Hausdorff distance between samples.