nils-herrmann/clean_scirepeval huggingface dataset:
Who Were the Early Adopters of Dabigatran?: An Application of Group-based Trajectory Models.

Variation in physician adoption of new medications is poorly understood. Traditional approaches (eg, measuring time to first prescription) may mask substantial heterogeneity in technology adoption.



Apply group-based trajectory models to examine the physician adoption of dabigratran, a novel anticoagulant.



A retrospective cohort study using prescribing data from IMS Xponent™ on all Pennsylvania physicians regularly prescribing anticoagulants (n=3911) and data on their characteristics from the American Medical Association Masterfile. We examined time to first dabigatran prescription and group-based trajectory models to identify adoption trajectories in the first 15 months. Factors associated with rapid adoption were examined using multivariate logistic regressions.



Trajectories of monthly share of oral anticoagulant prescriptions for dabigatran.



We identified 5 distinct adoption trajectories: 3.7% rapidly and extensively adopted dabigatran (adopting in ≤3 mo with 45% of prescriptions) and 13.4% were rapid and moderate adopters (≤3 mo with 20% share). Two groups accounting for 21.6% and 16.1% of physicians, respectively, were slower to adopt (6-10 mo post-introduction) and dabigatran accounted for <10% share. Nearly half (45.2%) of anticoagulant prescribers did not adopt dabigatran. Cardiologists were much more likely than primary care physicians to rapidly adopt [odds ratio (OR)=12.2; 95% confidence interval (CI), 9.27-16.1] as were younger prescribers (age 36-45 y: OR=1.49, 95% CI, 1.13-1.95; age 46-55: OR=1.34, 95% CI, 1.07-1.69 vs. >55 y).



Trajectories of physician adoption of dabigatran were highly variable with significant differences across specialties. Heterogeneity in physician adoption has potential implications for the cost and effectiveness of treatment.
Update on oral herpes virus infections.

Oral herpes virus infections (OHVIs) are among the most common mucosal disorders encountered by oral health care providers. These infections can affect individuals at any age, from infants to the elderly, and may cause significant pain and dysfunction. Immunosuppressed patients may be at increased risk for serious and potential life-threatening complications caused by OHVIs. Clinicians may have difficulty in diagnosing these infections because they can mimic other conditions of the oral mucosa. This article provides oral health care providers with clinically relevant information regarding etiopathogenesis, diagnosis, and management of OHVIs.
An Effective Path Load Balancing Mechanism Based on SDN

Path load balancing is used for distributing workload across an array of paths to increase network reliability and optimize link utilization. However, it is not easy to realize the load balancing globally in traditional networks as the whole status of the network is difficult to obtain. To address this problem, we propose the Fuzzy Synthetic Evaluation Mechanism (FSEM), a path load balancing solution based on Software Defined Networking (SDN). In this mechanism, the network traffic is allocated to the paths operated by Open Flow switches, where the flow-handling rules are installed by the central SDN controller. The paths can be dynamically adjusted with the aid of FSEM according to the global view of the network. Experimental results verify that the proposed solution can effectively balance the traffic and avoid unexpected breakdown caused by link failure. The overall network performance is also improved as well.
IMMEDIATE EFFECT OF 'NADI -SHODHANA PRANAYAMA' ON SOME SELECTED PARAMETERS OF CARDIOVASCULAR, PULMONARY, AND HIGHER FUNCTIONS OF BRAIN

Practice of pranayama has been known to modulate cardiac autonomic status with an improvement in cardio-respiratory functions. Keeping this in view, the present study is designed to determine whether Nadi-shodana pranayama practice for 20 minutes has any immediate effect on heart rate, systolic and diastolic blood pressure, peak expiratory flow rate, and simple problem solving ability. Ten normal healthy subjects of first year physiotherapy course volunteered for this study. They were aged between 17-20 years. Among them, five were females and five were males. They did not have any previous training in Pranayama. They were highly motivated to participate in this study program. Study procedures were done separately for each subject at the same time of the day between 4-5 pm. All the selected physiological parameters were measured before and after performing ‘Nadi-shodhana Pranayama’. Two sets of controls were done in the matched subjects by allowing them to relax in a couch (A) or close their eyes with quiet breathing for 20 minutes. Following nadi-shodhana pranayama of 20 minutes, a significant decline in basal heart rate (P<0.0001) and systolic blood pressure (P<0.001) was observed. Peak expiratory flow rate was significantly improved (P<0.01) and the time taken for simple problem solving was significantly less following pranayama practice (P<0.0001). In contrast, both control subjects did not show any significant change in respiratory and cardiovascular parameters with 20 minutes. The present study suggests that the ‘Nadi-shodhana Pranayama’ rapidly alters cardiopulmonary responses and improves simple problem solving. Further studies on a larger sample size need to illustrate the underlying mechanisms involved in this alteration.
A Latent-class Model for Estimating Product-choice Probabilities from Clickstream Data

This paper analyzes customer product-choice behavior based on the recency and frequency of each customer’s page views on e-commerce sites. Recently, we devised an optimization model for estimating product-choice probabilities that satisfy monotonicity, convexity, and concavity constraints with respect to recency and frequency. This shape-restricted model delivered high predictive performance even when there were few training samples. However, typical e-commerce sites deal in many different varieties of products, so the predictive performance of the model can be further improved by integration of such product heterogeneity. For this purpose, we develop a novel latent-class shape-restricted model for estimating product-choice probabilities for each latent class of products. We also give a tailored expectation-maximization algorithm for parameter estimation. Computational results demonstrate that higher predictive performance is achieved with our latent-class model than with the previous shape-restricted model and common latent-class logistic regression.
Applying supernode architecture for scalable multiplayer computer game

Scalability, fast response time and low cost are of utmost importance in designing a successful massively multiplayer online game. The underlying architecture plays an important role in meeting these conditions. Peer-to-peer architectures, have low infrastructure costs and can achieve high scalability, due to their distributed and collaborative nature. They can also achieve fast response times by creating direct connections between players. However, these architectures face many challenges. Therefore, the paper investigates existing peer to peer architecture solutions for a massively multiplayer online games. The study examines two hybrid architectures. In the first one, a supernode approach is used with a central server. In the contrast in the second one, there is no central server and pure peer to peer architecture is deployed. Moreover, the thesis proposes a solution based on multicast peer discovery and supernodes for a massively multiplayer online game. Also, all system is covered with simulation, that provides results for future analysing.
Filicide cases in Turkey, 1995-2000.

To determine socio-demographic features and criminal liability of individuals who committed filicide in Turkey.



The study involved 85 cases of filicide evaluated by the 4th Specialized Board of the Institute of Forensic Medicine in Istanbul in the 1995-2000 period. We assessed the characteristics of parents who committed filicide (age, sex, education level, employment status, and criminal liability) and children victims (age, sex, own or stepchild), as well as the causes of death.



There were 85 parents who committed filicide (41 fathers and 44 mothers) and 96 children victims. The mean age of mothers who committed filicide (52% of filicides) was 26.5-/+7.7 years, and the mean age of fathers (48% of filicides) was 36.1-/+10.0 years (t=-5.00, p<0.001). Individuals diagnosed with psychiatric disturbances, such as schizophrenia (61%), major depression (22%), imbecility (10%), and mild mental retardation (7%), were not subject to criminal liability. Almost half of parents who committed filicide were unemployed and illiterate.



Filicide in Turkey was equally committed by mothers and fathers. More than half of the parents were diagnosed with psychiatric disorders and came from disadvantageous socioeconomic environments, where unemployment and illiteracy rates are highly above the average of Turkey.
Technical Privacy Metrics: A Systematic Survey

The goal of privacy metrics is to measure the degree of privacy enjoyed by users in a system and the amount of protection offered by privacy-enhancing technologies. In this way, privacy metrics contribute to improving user privacy in the digital world. The diversity and complexity of privacy metrics in the literature make an informed choice of metrics challenging. As a result, instead of using existing metrics, new metrics are proposed frequently, and privacy studies are often incomparable. In this survey, we alleviate these problems by structuring the landscape of privacy metrics. To this end, we explain and discuss a selection of over 80 privacy metrics and introduce categorizations based on the aspect of privacy they measure, their required inputs, and the type of data that needs protection. In addition, we present a method on how to choose privacy metrics based on nine questions that help identify the right privacy metrics for a given scenario, and highlight topics where additional work on privacy metrics is needed. Our survey spans multiple privacy domains and can be understood as a general framework for privacy measurement.
Cybercrime in Nigeria

In this paper we investigated cybercrime and examined the relevant laws available to combat this crime in Nigeria. Therefore, we had a critical review of criminal laws in Nigeria and also computer network and internet security. The internet as an instrument to aid crime ranges from business espionage, to banking fraud, obtaining un-authorized and sabotaging data in computer networks of some key organizations. We investigated these crimes and noted some useful observations. From our observations, we profound solution to the inadequacies of existing enabling laws. Prevention of cybercrime requires the co-operation of all the citizens and not necessarily the police alone who presently lack specialists in its investigating units to deal with cybercrime. The eradication of this crime is crucial in view of the devastating effect on the image of Nigeria and the attendant consequence on the economy. Out of over 140 million Nigerians less than 5x10-4% are involved in cybercrime across Nigeria.
Journal of Homeland Security and Emergency Management Disaster Resilience Indicators for Benchmarking Baseline Conditions

There is considerable federal interest in disaster resilience as a mechanism for mitigating the impacts to local communities, yet the identification of metrics and standards for measuring resilience remain a challenge. This paper provides a methodology and a set of indicators for measuring baseline characteristics of communities that foster resilience. By establishing baseline conditions, it becomes possible to monitor changes in resilience over time in particular places and to compare one place to another. We apply our methodology to counties within the Southeastern United States as a proof of concept. The results show that spatial variations in disaster resilience exist and are especially evident in the rural/urban divide, where metropolitan areas have higher levels of resilience than rural counties. However, the individual drivers of the disaster resilience (or lack thereof)—social, economic, institutional, infrastructure, and community capacities—vary
FRACTAL ANTENNA FOR PASSIVE UHF RFID APPLICATIONS

This paper addresses the design of fractal antennas placed onto dielectric object in the UHF RFID band and introduces a tag antenna configuration of simple geometry having impedance tuning capability. Through the paper, the dimensions of the fractal antenna are optimized to improve the impedance matching with the chip impedance. The tag performance changes are studied when it is placed on different objects (e.g., cardboard boxes with various content), or when other objects are present in the vicinity of the tagged object. It has been shown that a tag antenna can be designed or tuned for optimum performance on a particular object. Using the finite element method the open circuit voltage and the polarization mismatch factor against the operating frequency are calculated. The input impedance, reflection coefficient, power transmission coefficient and the read range as a function of frequency are illustrated. The performance of the tag antenna in the presence of the dielectric box and different object materials inside the box is illustrated. The effect of the objects that are placed in the center of the dielectric box didn’t have a significant effect on the performance of the tag antenna; there is a small shift in the resonance frequency but still within the operating frequency band. Both the power transmission coefficient and the read range change with the object material. The backscattering properties of the tag antenna have been studied. The differential radar cross-section of the tag antenna is calculated for different antenna loads.
Implementation of data driven control system of DC motor by using system identification process

This paper presents data driven control system of DC motor by using system identification process. In this paper we use component base modeling similar to real DC motor by using simscape electronic systems for obtaining the input voltage and output speed of DC motor, the system identification toolbox and the nonlinear autoregressive with exogenous input (NARX) neural network for identification and obtaining the model of an object. The object model and training the neural network for data driven control system are developed by using MATLAB/SIMULINK platform. So, simulation results of this paper present the advantage of the suggested control method and the acceptable accuracy with respect to dynamic characteristics of the system.
Building Data-driven Models with Microstructural Images: Generalization and Interpretability

As data-driven methods rise in popularity in materials science applications, a key question is how these machine learning models can be used to understand microstructure. Given the importance of process-structureproperty relations throughout materials science, it seems logical that models that can leverage microstructural data would be more capable of predicting property information. While there have been some recent attempts to use convolutional neural networks to understand microstructural images, these early studies have focused only on which featurizations yield the highest machine learning model accuracy for a single data set. This paper explores the use of convolutional neural networks for classifying microstructure with a more holistic set of objectives in mind: generalization between data sets, number of features required, and interpretability.
MATATABI: Multi-layer Threat Analysis Platform with Hadoop

Threat detection and analysis are indispensable processes in today's cyberspace, but current state of the art threat detection is still limited to specific aspects of modern malicious activities due to the lack of information to analyze. By measuring and collecting various types of data, from traffic information to human behavior, at different vantage points for a long duration, the viewpoint seems to be helpful to deeply inspect threats, but faces scalability issues as the amount of collected data grows, since more computational resources are required for the analysis. In this paper, we report our experience from operating the Hadoop platform, called MATATABI, for threat detections, and present the micro-benchmarks with four different backends of data processing in typical use cases such as log data and packet trace analysis. The benchmarks demonstrate the advantages of distributed computation in terms of performance. Our extensive use cases of analysis modules showcase the potential benefit of deploying our threat analysis platform.
A Novel Classification Framework for Evaluating Individual and Aggregate Diversity in Top-N Recommendations

The primary goal of a recommender system is to generate high quality user-centred recommendations. However, the traditional evaluation methods and metrics were developed before researchers understood all the factors that increase user satisfaction. This study is an introduction to a novel user and item classification framework. It is proposed that this framework should be used during user-centred evaluation of recommender systems and the need for this framework is justified through experiments. User profiles are constructed and matched against other users’ profiles to formulate neighbourhoods and generate top-N recommendations. The recommendations are evaluated to measure the success of the process. In conjunction with the framework, a new diversity metric is presented and explained. The accuracy, coverage, and diversity of top-N recommendations is illustrated and discussed for groups of users. It is found that in contradiction to common assumptions, not all users suffer as expected from the data sparsity problem. In fact, the group of users that receive the most accurate recommendations do not belong to the least sparse area of the dataset.
An improved corpus of disease mentions in PubMed citations

The latest discoveries on diseases and their diagnosis/treatment are mostly disseminated in the form of scientific publications. However, with the rapid growth of the biomedical literature and a high level of variation and ambiguity in disease names, the task of retrieving disease-related articles becomes increasingly challenging using the traditional keywordbased approach. An important first step for any disease-related information extraction task in the biomedical literature is the disease mention recognition task. However, despite the strong interest, there has not been enough work done on disease name identification, perhaps because of the difficulty in obtaining adequate corpora. Towards this aim, we created a large-scale disease corpus consisting of 6900 disease mentions in 793 PubMed citations, derived from an earlier corpus. Our corpus contains rich annotations, was developed by a team of 12 annotators (two people per annotation) and covers all sentences in a PubMed abstract. Disease mentions are categorized into Specific Disease, Disease Class, Composite Mention and Modifier categories. When used as the gold standard data for a state-of-the-art machine-learning approach, significantly higher performance can be found on our corpus than the previous one. Such characteristics make this disease name corpus a valuable resource for mining disease-related information from biomedical text. The NCBI corpus is available for download at http://www.ncbi.nlm.nih.gov/CBBresearch/Fe llows/Dogan/disease.html.
A review on image processing and image segmentation

A methodological study on significance of image processing and its applications in the field of computer vision is carried out here. During an image processing operation the input given is an image and its output is an enhanced high quality image as per the techniques used. Image processing usually referred as digital image processing, but optical and analog image processing also are possible. Our study provides a solid introduction to image processing along with segmentation techniques, computer vision fundamentals and its applied applications that will be of worth to the image processing and computer vision research communities.
A novel capacitive type torque sensor for robotic applications

This paper presents a novel capacitive type joint torque sensor for robotic applications. The proposed torque sensor enables to measure the rotational torque value while it decouples the external Force/Torque loads without any complicated computing procedure. To measure the joint torque, just two capacitive transducer cells are used. These two cells are located in the opposite sides of each other, which compensates the cross couplings of the torques when external Force/Torque loads are applied. To simply the manufacturing process, the proposed sensor is designed to be composed of three plate-shaped parts and a single printed circuit board (PCB). Lastly, the developed torque sensor is manufactured and its performances are experimentally demonstrated.
Table-processing paradigms: a research survey

Tables are a ubiquitous form of communication. While everyone seems to know what a table is, a precise, analytical definition of “tabularity” remains elusive because some bureaucratic forms, multicolumn text layouts, and schematic drawings share many characteristics of tables. There are significant differences between typeset tables, electronic files designed for display of tables, and tables in symbolic form intended for information retrieval. Most past research has addressed the extraction of low-level geometric information from raster images of tables scanned from printed documents, although there is growing interest in the processing of tables in electronic form as well. Recent research on table composition and table analysis has improved our understanding of the distinction between the logical and physical structures of tables, and has led to improved formalisms for modeling tables. This review, which is structured in terms of generalized paradigms for table processing, indicates that progress on half-a-dozen specific research issues would open the door to using existing paper and electronic tables for database update, tabular browsing, structured information retrieval through graphical and audio interfaces, multimedia table editing, and platform-independent display.
IoT Data Compression: Sensor-Agnostic Approach

Management of bulk sensor data is one of the challenging problems in the development of Internet of Things (IoT) applications. High volume of sensor data induces for optimal implementation of appropriate sensor data compression technique to deal with the problem of energy-efficient transmission, storage space optimization for tiny sensor devices, and cost-effective sensor analytics. The compression performance to realize significant gain in processing high volume sensor data cannot be attained by conventional lossy compression methods, which are less likely to exploit the intrinsic unique contextual characteristics of sensor data. In this paper, we propose SensCompr, a dynamic lossy compression method specific for sensor datasets and it is easily realizable with standard compression methods. Senscompr leverages robust statistical and information theoretic techniques and does not require specific physical modeling. It is an information-centric approach that exhaustively analyzes the inherent properties of sensor data for extracting the embedded useful information content and accordingly adapts the parameters of compression scheme to maximize compression gain while optimizing information loss. Senscompr is successfully applied to compress large sets of heterogeneous real sensor datasets like ECG, EEG, smart meter, accelerometer. To the best of our knowledge, for the first time 'sensor information content'-centric dynamic compression technique is proposed and implemented particularly for IoT-applications and this method is independent to sensor data types.