nils-herrmann/clean_scirepeval huggingface dataset:
Efficient Scale Invariant Human Detection Using Histogram of Oriented Gradients for IoT Services

Recent advancements in computer vision, multimedia and Internet of Things (IoT) have shown that human detection methods are useful for applications of intelligent transportation system in smart environment. However, detection of a human in real world remains a challenging problem. Histogram of oriented gradients (HOG) based human detection gives an emphasis towards finding an effective solution to the problems of significant changes in view point, fixed resolution human but are expensive to compute. The proposed algorithm aims to reduce the computations using approximation methods and adapts for varying scale. The features are modeled at different scales for training the classifier. Experiments have been conducted on human datasets to demonstrate the superior performance of the proposed approach in human detection and discussions are made to integrate and increase personalization for building smart environment using IoT.
Measuring Data Quality: A Review of the Literature between 2005 and 2013

A literature review was done within a revision of a guideline concerned with data quality management in registries and cohort studies. The review focused on quality indicators, feedback, and source data verification. Thirty-nine relevant articles were selected in a stepwise selection process. The majority of the papers dealt with indicators. The papers presented concepts or data analyses. The leading indicators were related to case or data completeness, correctness, and accuracy. In the future, data pools as well as research reports from quantitative studies should be obligatory supplemented by information about their data quality, ideally picking up some indicators presented in this review.
Robust CFAR Detector Based on Truncated Statistics in Multiple-Target Situations

A new and robust constant false alarm rate (CFAR) detector based on truncated statistics (TSs) is proposed for ship detection in single-look intensity and multilook intensity synthetic aperture radar data. The approach is aimed at high-target-density situations such as busy shipping lines and crowded harbors, where the background statistics are estimated from potentially contaminated sea clutter samples. The CFAR detector uses truncation to exclude possible statistically interfering outliers and TSs to model the remaining background samples. The derived truncated statistic CFAR (TS-CFAR) algorithm does not require prior knowledge of the interfering targets. The TS-CFAR detector provides accurate background clutter modeling, a stable false alarm regulation property, and improved detection performance in high-target-density situations.
Automatic irrigation system using Arduino UNO

Now a days its a challenge to improve development of plant in respect of its growth and to reduce costs which leads to an innovative idea of using an automated irrigation system which will further help in better management of water and human resources. An automated irrigation system have been developed using sensors technology with Arduino to efficiently utilize water for irrigation purpose. The system has soil moisture sensor inserted into the soil of the plants and a water level sensor placed in a water container from where water will be pumped to plants for irrigation. An algorithm has been build out with threshold values of soil moisture sensor to control the water quantity in soil and also a water level sensor has been implemented to measure the water level in tank. This project requires Arduino board having inbuilt ATMega328 microcontroller. This project is need of the hour to convert manual irrigation into an automated irrigation which with the help of soil moisture sensor will detect dankness content of soil leading to turn ON/OFF of pumping motor. Human efforts can be reduced using this technique and increase saving of water by efficiently irrigating the plants. The design has been made with better resource management and low power consumption. This project brings into play a micro-controller which is of 8051 family, this programmable micro-controller collects the input signals converted into values of moisture in the soil via soil moisture sensors. As the microcontroller starts obtaining the signals, it creates an output that forces a relay for running the water pumping motor. An LCD screen is also linked to the micro-controller to show moisture conditions of the soil and water pump. The water level sensor is used to detect the level of tank so that tank contains efficient water to transfer into crops.
The Spinning Top Model, a New Path to Conceptualize Culture and Values: Applications to IS Research

In this conceptual study we first categorize, from existing literature, different conceptions of culture rooted in Anthropology and Sociology. We argue that these conceptions build up the logical structure of specific theoretical and empirical tools which address human/IS interactions in a cultural-based perspective. We then propose a new model of the individual’s global culture, the Spinning Top Model. We posit theoretical proposals based on this model and define a new analytical framework which can open new paths for IS research, the study of IT-related values , IT-attitudes and IT-behaviors.
Neuro-musculoskeletal and performance adaptations to lower-extremity plyometric training.

Plyometric training (PLY) is a very popular form of physical conditioning of healthy individuals that has been extensively studied over the last 3 decades. In this article, we critically review the available literature related to lower-body PLY and its effects on human neural and musculoskeletal systems, athletic performance and injury prevention. We also considered studies that combined lower-body PLY with other popular training modalities, as well as studies that applied PLY on non-rigid surfaces. The available evidence suggests that PLY, either alone or in combination with other typical training modalities, elicits numerous positive changes in the neural and musculoskeletal systems, muscle function and athletic performance of healthy individuals. Specifically, the studies have shown that long-term PLY (i.e. 3-5 sessions a week for 5-12 months) represents an effective training method for enhancing bone mass in prepubertal/early pubertal children, young women and premenopausal women. Furthermore, short-term PLY (i.e. 2-3 sessions a week for 6-15 weeks) can change the stiffness of various elastic components of the muscle-tendon complex of plantar flexors in both athletes and non-athletes. Short-term PLY also improves the lower-extremity strength, power and stretch-shortening cycle (SSC) muscle function in healthy individuals. These adaptive changes in neuromuscular function are likely the result of (i) an increased neural drive to the agonist muscles; (ii) changes in the muscle activation strategies (i.e. improved intermuscular coordination); (iii) changes in the mechanical characteristics of the muscle-tendon complex of plantar flexors; (iv) changes in muscle size and/or architecture; and (v) changes in single-fibre mechanics. Our results also show that PLY, either alone or in combination with other training modalities, has the potential to (i) enhance a wide range of athletic performance (i.e. jumping, sprinting, agility and endurance performance) in children and young adults of both sexes; and (ii) to reduce the risk of lower-extremity injuries in female athletes. Finally, available evidence suggests that short-term PLY on non-rigid surfaces (i.e. aquatic- or sand-based PLY) could elicit similar increases in jumping and sprinting performance as traditional PLY, but with substantially less muscle soreness. Although many issues related to PLY remain to be resolved, the results of this review allow us to recommend the use of PLY as a safe and effective training modality for improving lower-extremity muscle function and functional performance of healthy individuals. For performance enhancement and injury prevention in competitive sports, we recommend an implementation of PLY into a well designed, sport-specific physical conditioning programme.
Thyroid associated orbitopathy

Thyroid associated orbitopathy, also known as Graves' orbitopathy, is typically a self-limiting autoimmune process associated with dysthyroid states. The clinical presentation may vary from very mild disease to severe irreversible sight-threatening complications. Despite ongoing basic science and clinical research, the pathogenesis and highly effective therapeutic strategies remain elusive. The present article reviews the pathophysiology, clinical presentation, and management of this common, yet poorly understood disease, which remains a challenge to the ophthalmologist.
Scarlett: coping with skewed content popularity in mapreduce clusters

To improve data availability and resilience MapReduce frameworks use file systems that replicate data uniformly. However, analysis of job logs from a large production cluster shows wide disparity in data popularity. Machines and racks storing popular content become bottlenecks; thereby increasing the completion times of jobs accessing this data even when there are machines with spare cycles in the cluster. To address this problem, we present Scarlett, a system that replicates blocks based on their popularity. By accurately predicting file popularity and working within hard bounds on additional storage, Scarlett causes minimal interference to running jobs. Trace driven simulations and experiments in two popular MapReduce frameworks (Hadoop, Dryad) show that Scarlett effectively alleviates hotspots and can speed up jobs by 20.2%.
HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles for Time Series Classification

There have been many new algorithms proposed over the last five years for solving time series classification (TSC) problems. A recent experimental comparison of the leading TSC algorithms has demonstrated that one approach is significantly more accurate than all others over 85 datasets. That approach, the Flat Collective of Transformation-based Ensembles (Flat-COTE), achieves superior accuracy through combining predictions of 35 individual classifiers built on four representations of the data into a flat hierarchy. Outside of TSC, deep learning approaches such as convolutional neural networks (CNN) have seen a recent surge in popularity and are now state of the art in many fields. An obvious question is whether CNNs could be equally transformative in the field of TSC. To test this, we implement a common CNN structure and compare performance to Flat-COTE and a recently proposed time series-specific CNN implementation. We find that Flat-COTE is significantly more accurate than both deep learning approaches on 85 datasets. These results are impressive, but Flat-COTE is not without deficiencies. We improve the collective by adding new components and proposing a modular hierarchical structure with a probabilistic voting scheme that allows us to encapsulate the classifiers built on each transformation. We add two new modules representing dictionary and interval-based classifiers, and significantly improve upon the existing frequency domain classifiers with a novel spectral ensemble. The resulting classifier, the Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is significantly more accurate than Flat-COTE and represents a new state of the art for TSC. HIVE-COTE captures more sources of possible discriminatory features in time series and has a more modular, intuitive structure.
System usage behavior as a proxy for user satisfaction: an empirical investigation

Organizations are increasingly recognizing that user satisfaction with information systems is one of the most important determinants of the success of those systems. However, current satisfaction measures involve an intrusion into the users' worlds, and are frequently deemed to be too cumbersome to be justi®ed ®nancially and practically. This paper describes a methodology designed to solve this contemporary problem. Based on theory which suggests that behavioral observations can be used to measure satisfaction, system usage statistics from an information system were captured around the clock for 6 months to determine users' satisfaction with the system. A traditional satisfaction evaluation instrument, a validated survey, was applied in parallel, to verify that the analysis of the behavioral data yielded similar results. The ®nal results were analyzed statistically to demonstrate that behavioral analysis is a viable alternative to the survey in satisfaction measurement.
An Improved Extended Kalman Filter for Localization of a Mobile Node with NLOS Anchors

Tracking a mobile node using a wireless sensor network under non-line of sight (NLOS) conditions, has been considered in this work, which is of interest to indoor positioning applications. A hybrid of time difference of arrival (TDOA) and angle of arrival (AOA) measurements, suitable for tracking asynchronous targets, is exploited. The NLOS biases of the TDOA measurements and the position and velocity of the target are included in the state vector. To track the latter, we use a modified form of the extended Kalman filter (EKF) with bound constraints on the NLOS biases, as derived from geometrical considerations. Through simulations, we show that our technique can outperform the EKF and the memoryless constrained optimization techniques. Extended Kalman filter; localization; non-line of sight; ultra wideband.
Self-adaptation of parameters in a learning classifier system ensemble machine

Self-adaptation is a key feature of evolutionary algorithms (EAs). Although EAs have been used successfully to solve a wide variety of problems, the performance of this technique depends heavily on the selection of the EA parameters. Moreover, the process of setting such parameters is considered a time-consuming task. Several research works have tried to deal with this problem; however, the construction of algorithms letting the parameters adapt themselves to the problem is a critical and open problem of EAs. This work proposes a novel ensemble machine learning method that is able to learn rules, solve problems in a parallel way and adapt parameters used by its components. A self-adaptive ensemble machine consists of simultaneously working extended classifier systems (XCSs). The proposed ensemble machine may be treated as a meta classifier system. A new self-adaptive XCS-based ensemble machine was compared with two other XCSbased ensembles in relation to one-step binary problems: Multiplexer, One Counts, Hidden Parity, and randomly generated Boolean functions, in a noisy version as well. Results of the experiments have shown the ability of the model to adapt the mutation rate and the tournament size. The results are analyzed in detail.
The Prevention of Child and Adolescent Anxiety: A Meta-analytic Review

The purpose of this study was to provide a comprehensive review of the effectiveness of child and adolescent anxiety prevention programs. Mean weighted effect sizes were calculated, and studies were encoded for potential moderator variables. A statistically significant effect size of .18 was obtained at post-intervention, which is consistent with effect sizes reported in reviews of depression, eating disorder, and substance abuse prevention programs. However, the effect sizes obtained at follow-up yielded mixed results. Significant moderators of program effectiveness were found including provider type (professional versus lay provider) and the use of the FRIENDS program. In contrast, program duration, participant age, gender, and program type (universal versus targeted) were not found to moderate program effectiveness. Clinical implications and directions for future research are discussed, including the need for more long-term follow-up, early prevention programs, and studies that systematically examine the impact of parent involvement on program effectiveness.
Experimental Study on Low Power Wide Area Networks (LPWAN) for Mobile Internet of Things

In the past decade, we have witnessed explosive growth in the number of low-power embedded and Internet-connected devices, reinforcing the new paradigm, Internet of Things (IoT). The low power wide area network (LPWAN), due to its long-range, low-power and low-cost communication capability, is actively considered by academia and industry as the future wireless communication standard for IoT. However, despite the increasing popularity of `mobile IoT', little is known about the suitability of LPWAN for those mobile IoT applications in which nodes have varying degrees of mobility. To fill this knowledge gap, in this paper, we conduct an experimental study to evaluate, analyze, and characterize LPWAN in both indoor and outdoor mobile environments. Our experimental results indicate that the performance of LPWAN is surprisingly susceptible to mobility, even to minor human mobility, and the effect of mobility significantly escalates as the distance to the gateway increases. These results call for development of new mobility-aware LPWAN protocols to support mobile IoT.
Estimating fetal age: computer-assisted analysis of multiple fetal growth parameters.

Regression models for predicting menstrual age based on real-time sonographic measurements of four fetal parameters (biparietal diameter, head circumference, abdominal circumference, and femur length), used alone and in combination, were developed in a cross-sectional study of 361 fetuses between 14 and 42 menstrual weeks. The head circumference and femur length were the strongest individual predictors of age. A number of combinations of fetal parameters, including the combination of head circumference and femur length, provided age estimates that were significantly better (p = 0.05) than those using any single parameter alone. It was also demonstrated that simply averaging individual age estimates in a given case could provide results that were not significantly different from those obtained by using the same parameters in a complex regression equation. The advantages and potential pitfalls of this system of fetal dating are discussed.
The discovery of artemisinin (qinghaosu) and gifts from Chinese medicine

Joseph Goldstein has written in this journal that creation (through invention) and revelation (through discovery) are two different routes to advancement in the biomedical sciences1. In my work as a phytochemist, particularly during the period from the late 1960s to the 1980s, I have been fortunate enough to travel both routes. I graduated from the Beijing Medical University School of Pharmacy in 1955. Since then, I have been involved in research on Chinese herbal medicine in the China Academy of Chinese Medical Sciences (previously known as the Academy of Traditional Chinese Medicine). From 1959 to 1962, I was released from work to participate in a training course in Chinese medicine that was especially designed for professionals with backgrounds in Western medicine. The 2.5-year training guided me to the wonderful treasure to be found in Chinese medicine and toward understanding the beauty in the philosophical thinking that underlies a holistic view of human beings and the universe.
Sodium benzoate-rich beverage consumption is associated with increased reporting of ADHD symptoms in college students: a pilot investigation.

Sodium benzoate, a common additive in popular beverages, has recently been linked to ADHD. This research examined the relationship between sodium benzoate-rich beverage ingestion and symptoms related to ADHD in college students.



College students (N = 475) completed an anonymous survey in class in fall 2010. The survey assessed recent intake of a noninclusive list of sodium benzoate-rich beverages and ADHD-related symptoms using a validated screener.



Sodium benzoate-rich beverage intake was significantly associated with ADHD-related symptoms (p = .001), and significance was retained after controlling for covariates. Students scoring ≥4 on the screener (scores that may be consistent with ADHD; n = 67) reported higher intakes (34.9 ± 4.4 servings/month) than the remainder of the sample (16.7 ± 1.1 servings/month).



These data suggest that a high intake of sodium benzoate-rich beverages may contribute to ADHD-related symptoms in college students and warrants further investigation.
Women's Choice of Positions during Labour: Return to the Past or a Modern Way to Give Birth? A Cohort Study in Italy

Childbirth medicalization has reduced the parturient's opportunity to labour and deliver in a spontaneous position, constricting her to assume the recumbent one. The aim of the study was to compare recumbent and alternative positions in terms of labour process, type of delivery, neonatal wellbeing, and intrapartum fetal head rotation.



We conducted an observational cohort study on women at pregnancy term. Primiparous women with physiological pregnancies and single cephalic fetuses were eligible for the study. We considered data about maternal-general characteristics, labour process, type of delivery, and neonatal wellbeing at birth. Patients were divided into two groups: Group-A if they spent more than 50% of labour in a recumbent position and Group-B when in alternative ones.



225 women were recruited (69 in Group-A and 156 in Group-B). We found significant differences between the groups in terms of labour length, Numeric Rating Scale score and analgesia request rate, type of delivery, need of episiotomy, and fetal occiput rotation. No differences were found in terms of neonatal outcomes.



Alternative maternal positioning may positively influence labour process reducing maternal pain, operative vaginal delivery, caesarean section, and episiotomy rate. Women should be encouraged to move and deliver in the most comfortable position.
Edge preserving image enhancement for color and gray scale images using local mean and local standard deviation

The aim of image enhancement is to produce a processed image which is more suitable than the original image for specific application. Application can be edge detection, boundary detection, image fusion, segmentation etc. In this paper different types of image enhancement algorithms in spatial domain are presented for gray scale as well as for color images. Quantitative analysis like AMBE (Absolute mean brightness error), MSE (Mean square error) and PSNR (Peak signal to noise ratio) for the different algorithms are evaluated. For gray scale image Weighted histogram equalization, Linear contrast stretching (LCS), Non linear contrast stretching logarithmic (NLLCS), Non linear contrast stretching exponential (NLECS), Bi Histogram Equalization (BHE) algorithms are discussed and compared. For color image (RGB) Linear contrast stretching, Non linear contrast stretching logarithmic and Non linear contrast stretching exponential algorithms are discussed. During result analysis, it has been observed that some algorithms does give considerably highly distinct values(MSE or AMBE) for different images. To stabilize these parameters, had proposed the new enhancement scheme Local mean and local standard deviation(LMLS) which will take care of these issues. By experimental analysis It has been observed that proposed method gives better AMBE (should be less) and PSNR (should be high) values compared with other algorithms, also these values are not highly distinct for different images.