nils-herrmann/clean_scirepeval huggingface dataset:
Comprehensive School Physical Activity Programming and Classroom Behavior.
Venue: American journal of health behavior

The purpose of this study was to examine the effect of a comprehensive school physical activity program (CSPAP) on classroom behavior in low income children.  The participants included 1460 children recruited from 3 low income schools receiving governmental financial assistance. A total of 77 classrooms were observed across grades K through 6. Classrooms were observed one week prior to the implementation of CSPAP and at 6 weeks and 12 weeks after commencement of the program. Members of the research team observed classroom behavior using systematic observation, specifically a 5 second momentary time sampling procedure. A generalized linear mixed effects model was used to determine the change in odds of a classroom achieving at least 80% on task behavior following the implementation of CSPAP.  There were 7.49 (95% CI: 2.83, 19.79) greater odds of a classroom achieving 80% on task behavior at 6 weeks compared to baseline and a 27.93 (95% CI: 7.93, 98.29) greater odds of a classroom achieving 80% on task behavior at 12 weeks compared to baseline (p .001) CONCLUSIONS After the CSPAP was implemented, on task classroom behavior significantly improved across all grade levels.
Image database TID 2013 Peculiarities results and perspectives

This paper describes a recently created image database, TID2013, intended for evaluation of full reference visual quality assessment metrics. With respect to TID2008, the new database contains a larger number (3000) of test images obtained from 25 reference images, 24 types of distortions for each reference image, and 5 levels for each type of distortion. Motivations for introducing 7 new types of distortions and one additional level of distortions are given; examples of distorted images are presented. Mean opinion scores (MOS) for the new database have been collected by performing 985 subjective experiments with volunteers (observers) from five countries (Finland, France, Italy, Ukraine, and USA) The availability of MOS allows the use of the designed database as a fundamental tool for assessing the effectiveness of visual quality. Furthermore, existing visual quality metrics have been tested with the proposed database and the collected results have been analyzed using rank order correlation coefficients between MOS and considered metrics. These correlation indices have been obtained both considering the full set of distorted images and specific image subsets, for highlighting advantages and drawbacks of existing, state of the art, quality metrics. Approaches to thorough performance analysis for a given metric are presented to detect practical situations or distortion types for which this metric is not adequate enough to human perception. The created image database and the collected MOS values are freely available for downloading and utilization for scientific purposes.
Intravenous Lacosamide in Pediatric Status Epilepticus: An Open Label Efficacy and Safety Study.
Venue: Pediatric neurology

Lacosamide is an antiepilepsy drug approved by the Food and Drug Administration for patients aged 17 years and older for partial onset seizures as monotherapy or adjunctive therapy. We reviewed the use of intravenous lacosamide in children aged less than 17 years with status epilepticus.  Children who received at least one dose of intravenous lacosamide for status epilepticus at our tertiary care children's hospital from December 2011 to March 2014 were studied. Status epilepticus was defined as continuous seizure activity for longer than 20 minutes or two or more recurrent seizures without regaining baseline level of awareness. Efficacy was defined as seizure freedom or more than 50% reduction of seizures within 24 hours of administering lacosamide.  Nine children with a mean age of 5.7 years (range: three months to 16 years) were included. The mean initial or loading dose was 8.7 mg/kg, with seven of nine patients receiving a dose of 10 mg/kg. The average total amount of intravenous lacosamide administered within the initial 24 hours was 13.8 mg/kg. Lacosamide was found to be efficacious in seven of nine (77.8% patients. Four patients (44.4% became seizure free. Two patients continued to have status epilepticus within 24 hours of lacosamide administration. Bradycardia was observed in one patient.  In children with status epilepticus, intravenous lacosamide was efficacious in 78% of the patients and 44% become seizure free. In addition, no significant adverse reactions were observed. An appropriate safe, effective initial, or loading dose may be 10 mg/kg.
Seeing Color, Seeing Emotion, Seeing Moral Value

Defenders of moral perception have famously argued that seeing value is relevantly similar to seeing colour. Some critics think, however, that the analogy between colour seeing and value seeing breaks down in several crucial respects. Defenders of moral perception, these critics say, have not succeeded in providing examples of non moral perception that are relevantly analogous to cases of moral perception. Therefore, it can be doubted whether there is such a thing as moral perception at all. I argue that, although the analogy between colour perception and moral perception does indeed break down in several crucial respects, that conclusion does not weaken the case of defenders of moral perception, because better analogies are available. If defenders of moral perception seek to draw support from an analogy, then seeing emotion will protect them better against criticisms than will seeing colour.
Masculine Compromise: Migration, Family, and Gender in China

Acknowledgments 1.  Migration, Family, and Masculinity in Postsocialist China 2. Marginal Men and China's Grand Narratives 3. Striking a Balance: Courtship, Sexuality, and Marriage 4. Conjugal Power and Diverse Strategies 5. Housework and Respectability 6. Migration, Fatherhood, and Emotionality 7. Filial Piety from Afar: Migrant Sons Renegotiating Elderly Care 8.  A Feminist Framework of Changing Masculinity Bibliography Index
Audio Songs Classification Based on Music Patterns

In this work, effort has been made to classify audio songs based on their music pattern which helps us to retrieve the music clips based on listener's taste. This task is helpful in indexing and accessing the music clip based on listener's state. Seven main categories are considered for this work such as devotional, energetic, folk, happy, pleasant, sad and, sleepy. Forty music clips of each category for training phase and fifteen clips of each category for testing phase are considered; vibrato related features such as jitter and shimmer along with the mel frequency cepstral coefficients (MFCCs) statistical values of pitch such as min, max, mean, and standard deviation are computed and added to the MFCCs, jitter, and shimmer which results in a 19 dimensional feature vector. feedforward backpropagation neural network (BPNN) is used as a classifier due to its efficiency in mapping the nonlinear relations. The accuracy of 82 is achieved on an average for 105 testing clips.
Detection of largest possible repeated patterns in Indian audio songs using spectral features
Venue: 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

In the field of Content Based Music Information Retrieval (CB MIR) researchers are always looking for better ways to classify songs aside from the existing classifiers such as genre, mood, scale, tempo, etc. By determining a way to isolate and extract maximum length repeating patterns (MLRPs) in a music file, we can analyze them in order to describe another potential classifier: complexity. Extraction of repeating patterns would also allow users to easily extract ringtones from their favorite songs. In this paper, an effort has been made to describe a method to extract repeating patterns from a given music file through direct signal level as well as feature level comparison. These extracted patterns can be used as ringtones, or for analysis to determine complexity. Features such as mel frequency cepstral coefficients (MFCCs) modulation spectral features (MSFs) and jitter are computed to reduce the computational time observed in signal level comparison.
The Association between Social Media Use and Eating Concerns among US Young Adults.
Venue: Journal of the Academy of Nutrition and Dietetics

The etiology of eating concerns is multifactorial, and exposure to media messages is considered to be a contributor. Although traditional media, such as television and magazines, have been examined extensively in relation to eating concerns risk, the influence of social media has received relatively less attention.  To examine the association between social media use and eating concerns in a large, nationally representative sample of young adults.   Participants were 1,765 young adults aged 19 to 32 years who were randomly selected from a national probability based online nonvolunteer panel.  An eating concerns scale was adapted from two validated measures: the SCOFF Questionnaire and the Eating Disorder Screen for Primary Care. Social media use (including Facebook, Twitter, Google+ YouTube, LinkedIn, Instagram, Pinterest, Tumblr, Vine, Snapchat, and Reddit) was assessed using both volume (time per day) and frequency (visits per week) STATISTICAL ANALYSES To examine associations between eating concerns and social media use, ordered logistic regression was used, controlling for all covariates.  Compared with those in the lowest quartile, participants in the highest quartiles for social media volume and frequency had significantly greater odds of having eating concerns (adjusted odds ratio 2.18, 95% CI 1.50 to 3.17 and adjusted odds ratio 2.55, 95% CI 1.72 to 3.78, respectively) There were significant positive overall linear associations between the social media use variables and eating concerns (P<0.001)  The results from this study indicate a strong and consistent association between social media use and eating concerns in a nationally representative sample of young adults aged 19 to 32 years. This association was apparent whether social media use was measured as volume or frequency. Further research should assess the temporality of these associations. It would also be useful to examine more closely the influence of specific characteristics of social media use, including content related and contextual features.
The association between social media use and sleep disturbance among young adults.
Venue: Preventive medicine

Many factors contribute to sleep disturbance among young adults. Social media (SM) use is increasing rapidly, and little is known regarding its association with sleep disturbance.  In 2014 we assessed a nationally representative sample of 1788 U.S. young adults ages 19 32. SM volume and frequency were assessed by self reported minutes per day spent on SM (volume) and visits per week (frequency) using items adapted from the Pew Internet Research Questionnaire. We assessed sleep disturbance using the brief Patient Reported Outcomes Measurement Information System (PROMIS(r) sleep disturbance measure. Analyses performed in Pittsburgh utilized chi square tests and ordered logistic regression using sample weights in order to estimate effects for the total U.S.  In models that adjusted for all sociodemographic covariates, participants with higher SM use volume and frequency had significantly greater odds of having sleep disturbance. For example, compared with those in the lowest quartile of SM use per day, those in the highest quartile had an AOR of 1.95 (95% CI=1.37 2.79) for sleep disturbance. Similarly, compared with those in the lowest quartile of SM use frequency per week, those in the highest quartile had an AOR of 2.92 (95% CI=1.97 4.32) for sleep disturbance. All associations demonstrated a significant linear trend.  The strong association between SM use and sleep disturbance has important clinical implications for the health and well being of young adults. Future work should aim to assess directionality and to better understand the influence of contextual factors associated with SM use.
Current updates on laboratory techniques for the diagnosis of male reproductive failure
Venue: Asian journal of andrology

The incidence of male reproductive failure leading to infertility, whether due to delayed parenthood, environmental issues, genetic factors, drugs, etc. is increasing throughout the world. The diagnosis and prognosis of male subfertility have become a challenge. While the basic semen assessment has been performed for many years, a number of studies question the value of the traditional semen characteristics. This is partly due to inadequate methods and standardization, limited knowledge of technical requirements for quality assurance, and an incomplete understanding of what clinical information a semen assessment can provide. Laboratories currently performing semen and endocrine assessment show great variability. The World Health Organization (WHO) manual for the evaluation of semen has been the core of andrology and fertility evaluation that has helped in further development of this field over many years. These include the physical appearance of the ejaculate, assessments of sperm count, motility, vitality, morphology, and functional aspects of the sperm and semen sample. These tests also include male endocrine profile, biochemical evaluation of the semen, detection of antisperm antibodies in serum, the use of computer aided sperm analysis (CASA) sperm DNA integrity, and its damage due to oxidative stress. Assisted reproductive techniques (e.g. IVF, ICSI) have shown great success but are too expensive. Further development in this field with newer techniques and extensive training/instructions can improve accuracy and reduce variability, thus maintaining the quality and standards of such an evaluation. There is an urgent need to have standardized training centers and increased awareness in this area of men's health for reproductive success.
Investigation of Chemical Sensor Array Optimization Methods for DADSS

Nowadays, most major automobile manufacturers are very interested, and actively involved, in developing driver alcoholdetection system for safety (DADSS) that serves to prevent driving under the influence. DADSS measures the blood alcoholconcentration (BAC) from the driver's breath and limits the ignition of the engine of the vehicle if the BAC exceeds thereference value. In this study, to optimize the sensor array of the DADSS, we selected sensors by using three different meth ods, configured the sensor arrays, and then compared their performance. The Wilks' lambda, stepwise elimination and filtermethod (using a principal component) were used as the sensor selection methods  We compared the performance ofthe arrays, by using the selectivity and sensitivity as criteria, and Sammon mapping for the analysis of the cluster type ofeach gas. The sensor array configured by using the stepwise elimination method exhibited the highest sensitivity and selec tivity and yielded the best visual result after Sammon mapping. Sensor array, Optimization, Stepwise sensor elimination, Wilks' lambda
EXPERIMENTAL PROOF OF PRINCIPLE OF IN VEHICLE PASSIVE BREATH ALCOHOL ESTIMATION

The reported work is highly related to the DADSS (driver alcohol detection system for safety,  program, and other related initiatives aiming at the prevention of drunk driving. The possibility of breath alcohol estimation in highly diluted breath samples in a vehicle cabin by using carbon dioxide as a tracer gas to compensate for the dilution has been demonstrated and evaluated elsewhere [4 12] The infrared sensor technology developed by SenseAir AB, Sweden, is enabling unprecedented sensor performance  However, passive breath alcohol detection requiring no cooperation from the driver has remained a major technological challenge. The aim of the present investigation is to obtain experimental proof ofprinciple of completely passive, in vehicle estimation of breath alcohol concentration. A prototype sensor system has been integrated with the casing of the upper steering column within a vehicle. Human subjects, some of them intoxicated by alcohol, are instructed to enter the vehicle and perform a simulated driving task while breathing normally. Sensor signals corresponding to alcohol and CO2 concentration at the sensor position are recorded and analyzed off line. The sensor CO2 signal pattern includes peaks corresponding to increasing CO2 concentration in expired air reaching the sensor position after leaving the subject's mouth or nose. These peaks will coincide with peaks in the alcohol signal from an intoxicated subject. From the peak magnitudes an algorithm for breath alcohol estimation has been devised. The results indicate that peaks from normal breathing are readily detectable and quantifiable by the sensors, although the dilution factor DF (ratio between expired and actual concentration measured by the sensor) may be as high as several hundred at the steering column sensor position.
Low Resolution Face Recognition Across Variations in Pose and Illumination
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence

We propose a completely automatic approach for recognizing low resolution face images captured in uncontrolled environment. The approach uses multidimensional scaling to learn a common transformation matrix for the entire face which simultaneously transforms the facial features of the low resolution and the high resolution training images such that the distance between them approximates the distance had both the images been captured under the same controlled imaging conditions. Stereo matching cost is used to obtain the similarity of two images in the transformed space. Though this gives very good recognition performance, the time taken for computing the stereo matching cost is significant. To overcome this limitation, we propose a reference based approach in which each face image is represented by its stereo matching cost from a few reference images. Experimental evaluation on the real world challenging databases and comparison with the state of the art super resolution, classifier based and cross modal synthesis techniques show the effectiveness of the proposed algorithm.
Evaluation of Appropriate Readout Sequence for Renal MRI Perfusion Using ASTAR (ASL) Technique.
Venue: Nihon Hoshasen Gijutsu Gakkai zasshi

The aim of this study was to compare true steady state free precession (True SSFP) with fast field echo (FFE) as readout imaging sequences for renal arterial spin labeling (ASL) and to optimize the imaging condition. Renal ASL perfusion images were acquired using signal targeting with alternated radio frequency using asymmetric inversion slab (ASTAR) technique with respiratory triggering at 3T MRI system, using either 3D True SSFP or FFE as the readout sequence. Inversion time (TI) varied from 800 to 2400 ms. Appropriate flip angles were estimated for each sequence by simulating signal intensity (SI) The SI of the renal cortex, vertebral body, and intestinal tract were measured, and the contrast ratio of the cortex (CRcortex) or intestine (CRintestine) related to vertebra was calculated. The image quality of the kidneys, background signal suppression, and misregistration were evaluated by four point scales. As a result, in quantitative evaluation, the average of CRcortex of each TI (800, 1200, 1600, 2000, and 2400 msec) were 0.49, 0.57, 0.63, 0.63, and 0.56 in FFE, and 0.59, 0.71, 0.73, 0.73, and 0.68 in True SSFP, respectively. IN qualitative evaluation, ASL images with True SSFP readout were significantly better than those with FFE readout. In conclusion, True SSFP sequences will be recommended as read out imaging sequence for obtaining ASL image compared with FFE image.
Effects of stimulant and opiate drugs on driver behavior during lane change in a driving simulator

The fact that alcohol use impairs driving has been known since many years ago; however, the influence of other drugs on driving behavior is rather new. Driving behavior under the influence of amphetamine and opium has not been fully explored yet. Some researchers have shown that such drivers suffer from an incorrect estimation of distance to the lead vehicle, high risk taking, and high lateral position deviation. In this paper, the subjects were categorized in three groups: stimulant drug users; opiate drug users; and non drug users. 6 amphetamine user drivers (average 25 years old in the range of 22 35) 6 opium user drivers (average 32 years old in the range of 22 45) and 6 non drug users (average 27 years old in the range of 22 35) were tested in the CI003 driving simulator. They drove in lane changing scenario. The values of the mean absolute value of the steering wheel angular velocity, integral of path curve, the mean absolute value of the transverse velocity of the vehicle in the lateral direction, The average and standard deviation of duration of lane changing and the minimum lateral acceleration of optimized Bezier curve were computed in lane changing scenario. For classifying of these groups, we decided to use artificial neural network to separate features. An artificial neural network with higher than 83% of accuracy was trained to distinguish the features.
Privacy Preserving Spectral Analysis of Large Graphs in Public Clouds
Venue: AsiaCCS

Large graph datasets have become invaluable assets for studying problems in business applications and scientific research. These datasets, collected and owned by data owners, may also contain privacy sensitive information. When using public clouds for elastic processing, data owners have to protect both data ownership and privacy from curious cloud providers. We propose a cloud centric framework that allows data owners to efficiently collect graph data from the distributed data contributors, and privately store and analyze graph data in the cloud. Data owners can conduct expensive operations in untrusted public clouds with privacy and scalability preserved. The major contributions of this work include two privacy preserving approximate eigen decomposition algorithms (the secure Lanczos and Nystrom methods) for spectral analysis of large graph matrices, and a personalized privacy preserving data submission method based on differential privacy that allows for the trade off between data sparsity and privacy. For a N node graph, the proposed approach allows a data owner to finish the core operations with only O(N) client side costs in computation, storage, and communication. The expensive O(N2) operations are performed in the cloud with the proposed privacy preserving algorithms. We prove that our approach can satisfactorily preserve data privacy against the untrusted cloud providers. We have conducted an extensive experimental study to investigate these algorithms in terms of the intrinsic relationships among costs, privacy, scalability, and result quality.
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