nils-herrmann/clean_scirepeval huggingface dataset:
Effect of dopaminergic medication on speech dysfluency in Parkinson’s disease: a longitudinal study

Although speech dysfluencies have been hypothesized to be associated with abnormal function of dopaminergic system, the effects of dopaminergic medication on speech fluency in Parkinson’s disease (PD) have not been systematically studied. The aim of the present study was, therefore, to investigate the long-term effect of dopaminergic medication on speech fluency in PD. Fourteen de novo PD patients with no history of developmental stuttering and 14 age- and sex-matched healthy controls (HC) were recruited. PD subjects were examined three times; before the initiation of dopaminergic treatment and twice in following 6 years. The percentage of dysfluent words was calculated from reading passage and monolog. The amount of medication was expressed by cumulative doses of l-dopa equivalent. After 3–6 years of dopaminergic therapy, PD patients exhibited significantly more dysfluent events compared to healthy subjects as well as to their own speech performance before the introduction of dopaminergic therapy (p < 0.05). In addition, we found a strong positive correlation between the increased occurrence of dysfluent words and the total cumulative dose of l-dopa equivalent (r = 0.75, p = 0.002). Our findings indicate an adverse effect of prolonged dopaminergic therapy contributing to the development of stuttering-like dysfluencies in PD. These findings may have important implication in clinical practice, where speech fluency should be taken into account to optimize dopaminergic therapy.
An efficient multi-core implementation of the Jaya optimisation algorithm

In this work, we propose a hybrid parallel Jaya optimisation algorithm for a multi-core environment with the aim of solving large-scale global optimisation problems. The proposed algorithm is called HHCPJaya, and combines the hyper-population approach with the hierarchical cooperation search mechanism. The HHCPJaya algorithm divides the population into many small subpopulations, each of which focuses on a distinct block of the original population dimensions. In the hyper-population approach, we increase the small subpopulations by assigning more than one subpopulation to each core, and each subpopulation evolves independently to enhance the explorative and exploitative nature of the population. We combine this hyper-population approach with the two-level hierarchical cooperative search scheme to find global solutions from all subpopulations. Furthermore, we incorporate an additional updating phase on the respective subpopulations based on global solutions, with the aim of further improving the convergence rate and the quality of solutions. Several experiments applying the proposed parallel algorithm in different settings prove that it demonstrates sufficient promise in terms of the quality of solutions and the convergence rate. Furthermore, a relatively small computational effort is required to solve complex and large-scale optimisation problems.
OVARIAN HUGE SEROUS CYSTADENOMA IN ADOLESCENT GIRL: A CASE REPORT

Ovarian cysts are an extremely common gynecological problem in adolescent. Majority of ovarian cysts are benign with few cases being malignant. Ovarian serous cystadenoma are rare in children. A 14-year-old presented with abdominal pain and severe abdominal distention. She underwent laparotomy and after surgical removal, the mass was found to be ovarian serous cystadenoma on histology. In conclusions, germ cell tumors the most important causes for the giant ovarian masses in children. Epithelial tumors should not be forgotten in the differential diagnosis.  Adolescent; Ovarian Cysts/diagnosis*; Cystadenoma, Serous/surgery; Ovarian Neoplasms/surgery; Ovarian cystadenoma
Narcissism and social networking Web sites.

The present research examined how narcissism is manifested on a social networking Web site (i.e., Facebook.com). Narcissistic personality self-reports were collected from social networking Web page owners. Then their Web pages were coded for both objective and subjective content features. Finally, strangers viewed the Web pages and rated their impression of the owner on agentic traits, communal traits, and narcissism. Narcissism predicted (a) higher levels of social activity in the online community and (b) more self-promoting content in several aspects of the social networking Web pages. Strangers who viewed the Web pages judged more narcissistic Web page owners to be more narcissistic. Finally, mediational analyses revealed several Web page content features that were influential in raters' narcissistic impressions of the owners, including quantity of social interaction, main photo self-promotion, and main photo attractiveness. Implications of the expression of narcissism in social networking communities are discussed.
optimizations for embedded systems and FFT

CNNs have proven to be a very successful yet computationally expensive technique which made them slow to be adopted in mobile and embedded systems. There is a number of possible optimizations: minimizing the memory footprint, using lower precision and approximate computation, reducing computation cost of convolutions with FFTs. These have been explored recently and were shown to work. This project take ideas of using FFTs further and develops an alternative way to computing CNN – purely in frequency domain. As a side result it develops intuition about nonlinear elements: why do they work and how new types can be created.
802.11n under the Microscope

We present an experimental study of IEEE 802.11n (high throughput extension to the 802.11 standard) using commodity wireless hardware. 802.11n introduces a variety of new mechanisms including physical layer diversity techniques, channel bonding and frame aggregation mechanisms. Using measurements from our testbed, we analyze the fundamental characteristics of 802.11n links and quantify the gains of each mechanism under diverse scenarios. We show that the throughput of an 802.11n link can be severely degraded (up ≈85%) in presence of an 802.11g link. Our results also indicate that increased amount of interference due to wider channel bandwidths can lead to throughput degradation. To this end, we characterize the nature of interference due to variable channel widths in 802.11n and show that careful modeling of interference is imperative in such scenarios. Further, as a reappraisal of previous work, we evaluate the effectiveness of MAC level diversity in the presence of physical layer diversity mechanisms introduced by 802.11n.
CoRR: a computing research repository

This paper describes the decisions by which teh Association for Computing Machinery integrated good features from the Los Alamos e-print (physics) archive and from Cornell University's Networked Computer Science Technical Reference Library to form their own open, permanent, online “computing research repository” (CoRR). Submitted papers are not refereed and anyone can browse and extract CoRR material for free, so Corr's eventual success could revolutionize computer science publishing. But several serious challenges remain: some journals forbid online preprints, teh CoRR user interface is cumbersome, submissions are only self-indexed, (no professional library staff manages teh archive) and long-term funding is uncertain.
Monte Carlo smoothing for non-linear time series

We develop methods for performing smoothing computations in general state-space models. The methods rely on a particle representation of the filtering distributions, and their evolution through time using sequential importance sampling and resampling ideas. In particular, novel techniques are presented for generation of sample realizations of historical state sequences. This is carried out in a forwardfiltering backward-smoothing procedure which can be viewed as the non-linear, non-Gaussian counterpart of standard Kalman filter-based simulation smoothers in the linear Gaussian case. Convergence in the mean-squared error sense of the smoothed trajectories is proved, showing the validity of our proposed method. The methods are tested in a substantial application for the processing of speech signals represented by a time-varying autoregression and parameterised in terms of timevarying partial correlation coefficients, comparing the results of our algorithm with those from a simple smoother based upon the filtered trajectories.
Learning to Track for Spatio-Temporal Action Localization

We propose an effective approach for spatio-temporal action localization in realistic videos. The approach first detects proposals at the frame-level and scores them with a combination of static and motion CNN features. It then tracks high-scoring proposals throughout the video using a tracking-by-detection approach. Our tracker relies simultaneously on instance-level and class-level detectors. The tracks are scored using a spatio-temporal motion histogram, a descriptor at the track level, in combination with the CNN features. Finally, we perform temporal localization of the action using a sliding-window approach at the track level. We present experimental results for spatio-temporal localization on the UCF-Sports, J-HMDB and UCF-101 action localization datasets, where our approach outperforms the state of the art with a margin of 15%, 7% and 12% respectively in mAP.
Factors Influencing the Usage of an Electronic Book Collection: Size of the E-book Collection, the Student Population, and the Faculty Population

Alain R. Lamothe is E-Resource Librarian in the J.N. Desmarais Library at Laurentian University;  This paper presents the results of a quantitative and systematic investigation exploring online e-book usage at the J.N. Desmarais Library of Laurentian University over a 9-year period. The size of an e-book collection was determined to show evidence of an extremely strong relationship with the level of usage e-books experienced. Of all factors examined during the course of this study, it was the size of the collection that exhibited the strongest association to usage levels and would suggest just how important the size and content of a collection can be to patron acceptance and utilization. Of all student academic levels, doctoral students exhibited the strongest relationship with e-book usage, while undergraduate students showed signs of the weakest. Faculty demonstrated the overall weakest relationship with e-book usage.
Prevalence of radiological signs of femoroacetabular impingement in patients presenting with long-standing adductor-related groin pain.

A decreased range of motion (ROM) of the hip joint is known to predispose to athletic groin injury. Femoroacetabular impingement (FAI) of the hip leads to a reduced ROM. This study examined the prevalence of radiological signs of FAI in patients presenting with long-standing adductor-related groin pain (LSARGP).



Prospective case series.



Outpatient Sports Medicine Department.



34 athletes with LSARGP defined as pain on palpation of the proximal insertion of adductor muscle and a painful, resisted adduction test.



A clinician blinded to the results of the radiological assessment performed a physical examination: iliopsoas length, hip ROM and anterior hip impingement test. Anteroposterior pelvic radiographs were examined by a second blinded clinician for the presence of: pistol grip deformity, centrum-collum-diaphyseal angle, femoral head neck ratio, coxa profunda, protrusio acetabuli, lateral centre edge angle, acetabular index and cross-over sign.



The prevalence of radiological signs of FAI was 94% (64/68). The mean number of radiological signs in hips with LSARGP was 1.84 (range 0-4, SD 1.05) and 1.96 (range 0-5, SD 1.12) in asymptomatic groins (p=0.95). The anterior hip impingement test was positive in nine cases. There was no relationship with the number of radiological signs (p=0.95). There was no correlation between hip ROM and the number of radiological signs (p=0.37).



Radiological signs of FAI are frequently observed in patients presenting with LSARGP. Clinicians should be aware of this fact and the possible lack of correlation when assessing athletes with groin pain.
Design and Implementation of Active Band-Pass Filter for Low Frequency RFID ( Radio Frequency Identification ) System Miss

In this paper, an active fourth order band-pass filter for RFID reader is designed and simulated. An active bandpass filter is required for the front-end system because all signals outside the (10-20) kHz time-varying signal backscattered by the tag need to be rejected. The filter is constructed from some given specifications, one of which is, that the filter needs to have a Butterworth response. The architecture that will be used is the Sallen-Key. The values for the passive components are calculated, and the circuits are then simulated with Circit Maker to reach the final conclusion which will describe the results of the simulations compared to MATLAB simulation results. Keywords―Band-pass filter, Butterworth response, Circuit Maker, MATLAB, RFID (Radio Frequency Identification), Sallen-Key.
0414-001 2 APK File Dynamic Analysis Emulator Android OS Dalvik VM Analysis Report Static Analysis Post Processing Network Protocols Code Coverage

The smartphone industry has been one of the fastest growing technological areas in recent years. Naturally, the considerable market share of the Android OS and the diversity of app distribution channels besides the official Google Play Store has attracted the attention of malware authors. To deal with the increasing numbers of malicious Android apps in the wild, malware analysts typically rely on analysis tools to extract characteristic information about an app in an automated fashion. While the importance of such tools has been addressed by the research community , [24], , , the resulting prototypes remain limited in terms of analysis capabilities and availability. In this paper we present ANDRUBIS, a completely automated, publicly available and comprehensive analysis system for Android applications. ANDRUBIS combines static analysis techniques with dynamic analysis on both Dalvik VM and system level, as well as several stimulation techniques to increase code coverage.
ggplot2 - Elegant Graphics for Data Analysis

Following your need to always fulfil the inspiration to obtain everybody is now simple. Connecting to the internet is one of the short cuts to do. There are so many sources that offer and connect us to other world condition. As one of the products to see in internet, this website becomes a very available place to look for countless ggplot2 elegant graphics for data analysis sources. Yeah, sources about the books from countries in the world are provided.
1 Backpropagation : The Basic Theory

Since the publication of the PDP volumes in 1986,1 learning by backpropagation has become the most popular method of training neural networks. The reason for the popularity is the underlying simplicity and relative power of the algorithm. Its power derives from the fact that, unlike its precursors, the perceptron learning rule and the Widrow-Hoff learning rule, it can be employed for training nonlinear networks of arbitrary connectivity. Since such networks are often required for real-world applications, such a learning procedure is critical. Nearly as important as its power in explaining its popularity is its simplicity. The basic igea is old and simple; namely define an error function and use hill climbing (or gradient descent if you prefer going downhill) to find a set of weights which optimize performance on a particular task. The algorithm is so simple that it can be implemented in a few lines' of code, and there have been no doubt many thousands of implementations of the algorithm by now. The name back propagation actually comes from the term employed by Rosenblatt (1962) for his attempt to generalize the perceptron learning algorithm to the multilayer case. There were many attempts to generalize the perceptron learning procedure to multiple layers during the 1960s and 1970s, but none of them were especially successful. There appear to have been at least three independent inventions of the modem version of the back-propagation algorithm: Paul Werbos developed the basic idea in 1974 in a Ph.D. dissertation entitled
Closed-loop verification of medical devices with model abstraction and refinement

The design and implementation of software for medical devices is challenging due to the closed-loop interaction with the patient, which is a stochastic physical environment. The safety-critical nature and the lack of existing industry standards for verification make this an ideal domain for exploring applications of formal modeling and closed-loop analysis. The biggest challenge is that the environment model(s) have to be both complex enough to express the physiological requirements and general enough to cover all possible inputs to the device. In this effort, we use a dual chamber implantable pacemaker as a case study to demonstrate verification of software specifications of medical devices as timed-automata models in UPPAAL. The pacemaker model is based on the specifications and algorithm descriptions from Boston Scientific. The heart is modeled using timed automata based on the physiology of heart. The model is gradually abstracted with timed simulation to preserve properties. A manual Counter-Example-Guided Abstraction and Refinement (CEGAR) framework has been adapted to refine the heart model when spurious counter-examples are found. To demonstrate the closed-loop nature of the problem and heart model refinement, we investigated two clinical cases of Pacemaker Mediated Tachycardia and verified their corresponding correction algorithms in the pacemaker. Along with our tools for code generation from UPPAAL models, this effort enables model-driven design and certification of software for medical devices.
Predicting exchange rates with sentiment indicators: An empirical evaluation using text mining and multilayer perceptrons

Recent innovations in text mining facilitate the use of novel data for sentiment analysis related to financial markets, and promise new approaches to the field of behavioural finance. Traditionally, text mining has allowed a near-real time analysis of available news feeds. The recent dissemination of web 2.0 has seen a drastic increase of user participation, providing comments on websites, social networks and blogs, creating a novel source of rich and personal sentiment data potentially of value to behavioural finance. This study explores the efficacy of using novel sentiment indicators from MarketPsych, which analyses social media in addition to newsfeeds to quantify various levels of individual's emotions, as a predictor for financial time series returns of the Australian Dollar (AUD) - US Dollar (USD) exchange rate. As one of the first studies evaluating both news and social media sentiment indicators as explanatory variables for linear and nonlinear regression algorithms, our study aims to make an original contribution to behavioural finance, combining technical and behavioural aspects of model building. An empirical out-of-sample evaluation with multiple input structures compares multivariate linear regression models (MLR) with multilayer perceptron (MLP) neural networks for descriptive modelling. The results indicate that sentiment indicators are explanatory for market movements of exchange rate returns, with nonlinear MLPs showing superior accuracy over linear regression models with a directional out-of-sample accuracy of 60.26% using cross validation.
A Markov-based channel model algorithm for wireless networks

Techniques for modeling and simulating channel conditions play an essential role in understanding network protocol and application behavior. , we demonstrated that inaccurate modeling using a traditional analytical model yielded significant errors in error control protocol parameters choices. In this paper, we demonstrate that time-varying effects on wireless channels result in wireless traces which exhibit non-stationary behavior over small window sizes. We then present an algorithm that divides traces into stationary components in order to provide analytical channel models that, relative to traditional approaches, more accurately represent characteristics such as burstiness, statistical distribution of errors, and packet loss processes. Our algorithm also generates artificial traces with the same statistical characteristics as actual collected network traces. For validation, we develop a channel model for the circuit-switched data service in GSM and show that it: (1) more closely approximates GSM channel characteristics than a traditional Gilbert model and (2) generates artificial traces that closely match collected traces' statistics. Using these traces in a simulator environment enables future protocol and application testing under different controlled and repeatable conditions.
A Harmonic Mean Linear Discriminant Analysis for Robust Image Classification

Linear Discriminant Analysis (LDA) is a widely-used supervised dimensionality reduction method in computer vision and pattern recognition. In null space based LDA (NLDA), a well-known LDA extension, between-class distance is maximized in the null space of the within-class scatter matrix. However, there are some limitations in NLDA. Firstly, for many data sets, null space of within-class scatter matrix does not exist, thus NLDA is not applicable to those datasets. Secondly, NLDA uses arithmetic mean of between-class distances and gives equal consideration to all between-class distances, which makes larger between-class distances can dominate the result and thus limits the performance of NLDA. In this paper, we propose a harmonic mean based Linear Discriminant Analysis, Multi-Class Discriminant Analysis (MCDA), for image classification, which minimizes the reciprocal of weighted harmonic mean of pairwise between-class distance. More importantly, MCDA gives higher priority to maximize small between-class distances. MCDA can be extended to multi-label dimension reduction. Results on 7 single-label data sets and 4 multi-label data sets show that MCDA has consistently better performance than 10 other single-label approaches and 4 other multi-label approaches in terms of classification accuracy, macro and micro average F1 score.
Comparison of circular and rectangular coil transformer parameters for wireless Power Transfer based on Finite Element Analysis

The major parameters of transformers used in wireless Power Transfer (WPT) are self inductance, mutual inductance and coefficient of coupling. Due to unique and complex nature of these transformers, determining these parameters by calculation has limitations which however can be overcome by use of Finite Element Analysis (FEA). This paper presents and compares the parameters of a circular and rectangular coil transformer modelled under different conditions using Ansys Maxwell software. An introduction to WPT system and its transformer model is also presented.
Joint combination of point cloud and DSM for 3D building reconstruction using airborne laser scanner data

More and more cities are looking for service providers able to deliver 3D city models in a short time. Airborne laser scanning techniques make it possible to acquire a three-dimensional point cloud leading almost instantaneously to digital surface models (DSM), but these models are far from a topological 3D model needed by geographers or land surveyors. The aim of this paper is to present the pertinence and advantages of combining simultaneously the point cloud and the normalized DSM (nDSM) in the main steps of a building reconstruction approach. This approach has been implemented in order to exempt any additional data and to automate the process. The proposed workflow firstly extracts the off-terrain mask based on DSM. Then, it combines the point cloud and the DSM for extracting a building mask from the off-terrain. At last, based on the previously extracted building mask, the reconstruction of 3D flat roof models is carried out and analyzed.