nils-herrmann/clean_scirepeval huggingface dataset:
A Comparison of four pulpotomy techniques in primary molars: a long-term follow-up.

The study evaluated the effects of formocresol (FC), ferric sulphate (FS), calcium hydroxide (Ca[OH](2)), and mineral trioxide aggregate (MTA) as pulp dressing agents in pulpotomized primary molars. Sixteen children each with at least four primary molars requiring pulpotomy were selected. Eighty selected teeth were divided into four groups and treated with one of the pulpotomy agent. The children were recalled for clinical and radiographic examination every 6 months during 2 years of follow-up. Eleven children with 56 teeth arrived for clinical and radiographic follow-up evaluation at 24 months. The follow-up evaluations revealed that the success rate was 76.9% for FC, 73.3% for FS, 46.1% for Ca(OH)(2), and 66.6% for MTA. In conclusion, Ca(OH)(2)is less appropriate for primary teeth pulpotomies than the other pulpotomy agents. FC and FS appeared to be superior to the other agents. However, there was no statistically significant difference between the groups.
Modifying attitude and intention toward regular physical activity using protection motivation theory: a randomized controlled trial.

We were guided by the Protection Motivation Theory to test the motivational interviewing effects on attitude and intention of obese and overweight women to do regular physical activity. In a randomized controlled trial, we selected using convenience sampling 60 overweight and obese women attending health centres. The women were allocated to 2 groups of 30 receiving a standard weight-control programme or motivational interviewing. All constructs of the theory (perceived susceptibility, severity, self-efficacy and response efficacy) and all anthropometric characteristics (except body mass index) were significantly different between the groups at 3 study times. The strongest predictors of intention to do regular physical exercise were perceived response efficacy and attitude at 2- and 6-months follow-up. We showed that targeting motivational interviewing with an emphasis on Protection Motivation Theory constructs appeared to be beneficial for designing and developing appropriate intervention to improve physical activity status among women with overweight and obesity.
A task-driven approach to time scale detection in dynamic networks

For any stream of time-stamped edges that form a dynamic network, an important choice is the aggregation granularity that an analyst uses to bin the data. Picking such a windowing of the data is often done by hand, or left up to the technology that is collecting the data. However, the choice can make a big difference in the properties of the dynamic network. Finding a good windowing is the time scale detection problem. In previous work, this problem is often solved with an unsupervised heuristic. As an unsupervised problem, it is difficult to measure how well a given windowing algorithm performs. In addition, we show that there is little correlation between the quality of a windowing across different tasks. Therefore the time scale detection problem should not be handled independently from the rest of the analysis of the network. Given this, in accordance with standard supervised machine learning practices, we introduce new windowing algorithms that automatically adapt to the task the analyst wants to perform by treating windowing as a hyperparameter for the task, rather than using heuristics. This approach measures the quality of the windowing by how well a given task is accomplished on the resulting network. This also allows us, for the first time, to directly compare different windowing algorithms to each other, by comparing how well the task is accomplished using that windowing algorithm. We compare this approach to previous approaches and several baselines
A modeling framework for business process reengineering using big data analytics and a goal-orientation

A business process is a collection of activities to create more business values and its continuous improvement aligned with business goals is essential to survive in fast changing business environment. However, it is quite challenging to find out whether a change of business processes positively affects business goals or not, if there are problems in the changing, what the reasons of the problems are, what solutions exist for the problems and which solutions should be selected. Big data analytics along with a goal-orientation which helps find out insights from a large volume of data in a goal concept opens up a new way for an effective business process reengineering. In this paper, we suggest a novel modeling framework which consists of a conceptual modeling language, a process and a tool for effective business processes reengineering using big data analytics and a goal-oriented approach. The modeling language defines important concepts for business process reengineering with metamodels and shows the concepts with complementary views: Business Goal-Process-Big Analytics Alignment View, Transformational Insight View and Big Analytics Query View. Analyzers hypothesize problems and solutions of business processes by using the modeling language, and the problems and solutions will be validated by the results of Big Analytics Queries which supports not only standard SQL operation, but also analytics operation such as prediction. The queries are run in an execution engine of our tool on top of Spark which is one of big data processing frameworks. In a goal-oriented spirit, all concepts not only business goals and business processes, but also big analytics queries are considered as goals, and alternatives are explored and selections are made among the alternatives using trade-off analysis. To illustrate and validate our approach, we use an automobile logistics example, then compare previous work.
Realized heritability and repeatability of risk-taking behaviour in relation to avian personalities.

Personalities are general properties of humans and other animals. Different personality traits are phenotypically correlated, and heritabilities of personality traits have been reported in humans and various animals. In great tits, consistent heritable differences have been found in relation to exploration, which is correlated with various other personality traits. In this paper, we investigate whether or not risk-taking behaviour is part of these avian personalities. We found that (i) risk-taking behaviour is repeatable and correlated with exploratory behaviour in wild-caught hand-reared birds, (ii) in a bi-directional selection experiment on 'fast' and 'slow' early exploratory behaviour, bird lines tend to differ in risk-taking behaviour, and (iii) within-nest variation of risk-taking behaviour is smaller than between-nest variation. To show that risk-taking behaviour has a genetic component in a natural bird population, we bred great tits in the laboratory and artificially selected 'high' and 'low' risk-taking behaviour for two generations. Here, we report a realized heritability of 19.3 +/- 3.3% (s.e.m.) for risk-taking behaviour. With these results we show in several ways that risk-taking behaviour is linked to exploratory behaviour, and we therefore have evidence for the existence of avian personalities. Moreover, we prove that there is heritable variation in more than one correlated personality trait in a natural population, which demonstrates the potential for correlated evolution.
Learning is Not a Spectator Sport: Doing is Better than Watching for Learning from a MOOC

The printing press long ago and the computer today have made widespread access to information possible. Learning theorists have suggested, however, that mere information is a poor way to learn. Instead, more effective learning comes through doing. While the most popularized element of today's MOOCs are the video lectures, many MOOCs also include interactive activities that can afford learning by doing. This paper explores the learning benefits of the use of informational assets (e.g., videos and text) in MOOCs, versus the learning by doing opportunities that interactive activities provide. We find that students doing more activities learn more than students watching more videos or reading more pages. We estimate the learning benefit from extra doing (1 SD increase) to be more than six times that of extra watching or reading. Our data, from a psychology MOOC, is correlational in character, however we employ causal inference mechanisms to lend support for the claim that the associations we find are causal.
Repair of Auricular Defects.

Repairing defects of the auricle requires an appreciation of the underlying 3-dimensional framework, the flexible properties of the cartilages, and the healing contractile tendencies of the surrounding soft tissue. In the analysis of auricular defects and planning of their reconstruction, it is helpful to divide the auricle into subunits for which different techniques may offer better functional and aesthetic outcomes. This article reviews many of the reconstructive options for defects of the various auricular subunits.
Research Opportunities of LoRaWAN for Internet of Things Implementation

In the development of the world of the Internet of Things (IoT), a technology that offers a wide range with low power consumption or known as Low Power Wide Area (LPWA) technology, is the best choice today. One of the most popular technologies is LoRaWAN. This technology is most widely used in IoT implementation because it has advantages in the range of connectivity, low power consumption with very large network capacity and simple network management. LoRa's technology received a lot of attention in the last few years. Along with the increasing needs and complexity of problems faced in IoT market, some obstacles in the implementation of these technologies. This case identified provides an overview of LoRa technology in various implementation cases, as well as the advantages, limitations, opportunities and challenges for future research. LoRa can be considered as a good candidate in solving the complexity of the problems in the development of IoT in the next future.
OTA Testing in Multipath of Antennas and Wireless Devices With MIMO and OFDM

New over-the-air (OTA) measurement technology is wanted for quantitative testing of modern wireless devices for use in multipath. We show that the reverberation chamber emulates a rich isotropic multipath (RIMP), making it an extreme reference environment for testing of wireless devices. This thereby complements testing in anechoic chambers representing the opposite extreme reference environment: pure line-of-sight (LOS). Antenna diversity gain was defined for RIMP environments based on improved fading performance. This paper finds this RIMP-diversity gain also valid as a metric of the cumulative improvement of the 1% worst users randomly distributed in the RIMP environment. The paper argues that LOS in modern wireless systems is random due to randomness of the orientations of the users and their devices. This leads to the definition of cumulative LOS-diversity gain of the 1% worst users in random LOS. This is generally not equal to the RIMP-diversity gain. The paper overviews the research on reverberation chambers for testing of wireless devices in RIMP environments. Finally, it presents a simple theory that can accurately model measured throughput for a long-term evolution (LTE) system with orthogonal frequency-division multiplexing (OFDM) and multiple-input-multiple-output (MIMO), the effects of which can clearly be seen and depend on the controllable time delay spread in the chamber.
A Neural Network-Based Ensemble Approach for Spam Detection in Twitter

As the social networking sites get more popular, spammers target these sites to spread spam posts. Twitter is one of the most popular online social networking sites where users communicate and interact on various topics. Most of the current spam filtering methods in Twitter focus on detecting the spammers and blocking them. However, spammers can create a new account and start posting new spam tweets again. So there is a need for robust spam detection techniques to detect the spam at tweet level. These types of techniques can prevent the spam in real time. To detect the spam at tweet level, often features are defined, and appropriate machine learning algorithms are applied in the literature. Recently, deep learning methods are showing fruitful results on several natural language processing tasks. We want to use the potential benefits of these two types of methods for our problem. Toward this, we propose an ensemble approach for spam detection at tweet level. We develop various deep learning models based on convolutional neural networks (CNNs). Five CNNs and one feature-based model are used in the ensemble. Each CNN uses different word embeddings (Glove, Word2vec) to train the model. The feature-based model uses content-based, user-based, and n-gram features. Our approach combines both deep learning and traditional feature-based models using a multilayer neural network which acts as a meta-classifier. We evaluate our method on two data sets, one data set is balanced, and another one is imbalanced. The experimental results show that our proposed method outperforms the existing methods.
Leveraging Neighbor Attributes for Classification in Sparsely Labeled Networks

Many analysis tasks involve linked nodes, such as people connected by friendship links. Research on link-based classification (LBC) has studied how to leverage these connections to improve classification accuracy. Most such prior research has assumed the provision of a densely labeled training network. Instead, this article studies the common and challenging case when LBC must use a single sparsely labeled network for both learning and inference, a case where existing methods often yield poor accuracy. To address this challenge, we introduce a novel method that enables prediction via “neighbor attributes,” which were briefly considered by early LBC work but then abandoned due to perceived problems. We then explain, using both extensive experiments and loss decomposition analysis, how using neighbor attributes often significantly improves accuracy. We further show that using appropriate semi-supervised learning (SSL) is essential to obtaining the best accuracy in this domain and that the gains of neighbor attributes remain across a range of SSL choices and data conditions. Finally, given the challenges of label sparsity for LBC and the impact of neighbor attributes, we show that multiple previous studies must be re-considered, including studies regarding the best model features, the impact of noisy attributes, and strategies for active learning.
Lightcut interpolation

Many-light rendering methods replace multi-bounce light transport with direct lighting from many virtual point light sources to allow for simple and efficient computation of global illumination. Lightcuts build a hierarchy over virtual lights, so that surface points can be shaded with a sublinear number of lights while minimizing error. However, the original algorithm needs to run on every shading point of the rendered image. It is well known that the performance of Lightcuts can be improved by exploiting the coherence between individual cuts. We propose a novel approach where we invest into the initial lightcut creation at representative cache records, and then directly interpolate the input lightcuts themselves as well as per-cluster visibility for neighboring shading points. This allows us to improve upon the performance of the original Lightcuts algorithm by a factor of 4−8 compared to an optimized GPU-implementation of Lightcuts, while introducing only a small additional approximation error. The GPU-implementation of our technique enables us to create previews of Lightcuts-based global illumination renderings.
High gain design of a very small normal mode helical antenna for RFID tags

Recently RFID tags of 900 MHz band are interested in many commercial fields. Tags are requested to be used on liquid bottles, medicine bottles and metallic plates. So, antennas are requested to be made very small and to have properties capable of use in metal proximity. In this paper, important design subjects to achieve very small antennas are summarized and technical solutions are explained first of all. Following this summary, the normal mode helical antenna (NMHA) is selected as a promising antenna that is suitable for achieving high gain in very small size and metal proximity use. Next, useful design data of NMHA to achieve high gain and metal proximity are shown. And an example of achieving a low profile NMHA is shown. In order to ensure realization, measured results of a fabricated antenna is shown. Moreover, the fabricated antenna is applied to the commercial IC chip. Communication distances are compared with that of the commercial tags.
BayDNN: Friend Recommendation with Bayesian Personalized Ranking Deep Neural Network

Friendship is the cornerstone to build a social network. In online social networks, statistics show that the leading reason for user to create a new friendship is due to recommendation. Thus the accuracy of recommendation matters. In this paper, we propose a Bayesian Personalized Ranking Deep Neural Network (BayDNN) model for friend recommendation in social networks. With BayDNN, we achieve significant improvement on two public datasets: Epinions and Slashdot. For example, on Epinions dataset, BayDNN significantly outperforms the state-of-the-art algorithms, with a 5% improvement on NDCG over the best baseline.
 The advantages of the proposed BayDNN mainly come from its underlying convolutional neural network (CNN), which offers a mechanism to extract latent deep structural feature representations of the complicated network data, and a novel Bayesian personalized ranking idea, which precisely captures the users' personal bias based on the extracted deep features. To get good parameter estimation for the neural network, we present a fine-tuned pre-training strategy for the proposed BayDNN model based on Poisson and Bernoulli probabilistic models.
Super-resolution fluorescence microscopy.

Achieving a spatial resolution that is not limited by the diffraction of light, recent developments of super-resolution fluorescence microscopy techniques allow the observation of many biological structures not resolvable in conventional fluorescence microscopy. New advances in these techniques now give them the ability to image three-dimensional (3D) structures, measure interactions by multicolor colocalization, and record dynamic processes in living cells at the nanometer scale. It is anticipated that super-resolution fluorescence microscopy will become a widely used tool for cell and tissue imaging to provide previously unobserved details of biological structures and processes.
i-learning IoT: An intelligent self learning system for home automation using IoT

Internet of Things (IoT) is extension of current internet to provide communication, connection, and inter-networking between various devices or physical objects also known as “Things.” In this paper we have reported an effective use of IoT for Environmental Condition Monitoring and Controlling in Homes. We also provide fault detection and correction in any devices connected to this system automatically. Home Automation is nothing but automation of regular activities inside the home. Now a day's due to huge advancement in wireless sensor network and other computation technologies, it is possible to provide flexible and low cost home automation system. However there is no any system available in market which provide home automation as well as error detection in the devices efficiently. In this system we use prediction to find out the required solution if any problem occurs in any device connected to the system. To achieve that we are applying Data Mining concept. For efficient data mining we use Naive Bayes Classifier algorithm to find out the best possible solution. This gives a huge upper hand on other available home automation system, and we actually manage to provide a real intelligent system.
Customers churn prediction and marketing retention strategies . An application of support vector machines based on the AUC parameter-selection technique in B 2 B e-commerce industry

Article history: Received 24 January 2015 Received in revised form 12 August 2016  August 2016 Available online xxxx E-commerce has provided newopportunities for both businesses and consumers to easily share information,find and buy a product, increasing the ease of movement from one company to another as well as to increase the risk of churn. In this study we develop a churn prediction model tailored for B2B e-commerce industry by testing the forecasting capability of a newmodel, the support vector machine (SVM) based on the AUC parameter-selection technique (SVMauc). The predictive performance of SVMauc is benchmarked to logistic regression, neural network and classic support vector machine. Our study shows that the parameter optimization procedure plays an important role in the predictive performance and the SVMauc points out good generalization performance when applied to noisy, imbalance and nonlinear marketing data outperforming the other methods. Thus, our findings confirm that the data-driven approach to churn prediction and the development of retention strategies outperforms commonly used managerial heuristics in B2B e-commerce industry.
Robust Intensity-Based Localization Method for Autonomous Driving on Snow–Wet Road Surface

Autonomous vehicles are being developed rapidly in recent years. In advance implementation stages, many particular problems must be solved to bring this technology into the market place. This paper focuses on the problem of driving in snow and wet road surface environments. First, the quality of laser imaging detection and ranging (LIDAR) reflectivity decreases on wet road surfaces. Therefore, an accumulation strategy is designed to increase the density of online LIDAR images. In order to enhance the texture of the accumulated images, principal component analysis is used to understand the geometrical structures and texture patterns in the map images. The LIDAR images are then reconstructed using the leading principal components with respect to the variance distribution accounted by each eigenvector. Second, the appearance of snow lines deforms the expected road context in LIDAR images. Accordingly, the edge profiles of the LIDAR and map images are extracted to encode the lane lines and roadside edges. Edge matching between the two profiles is then calculated to improve localization in the lateral direction. The proposed method has been tested and evaluated using real data that are collected during the winter of 2016–2017 in Suzu and Kanazawa, Japan. The experimental results show that the proposed method increases the robustness of autonomous driving on wet road surfaces, provides a stable performance in laterally localizing the vehicle in the presence of snow lines, and significantly reduces the overall localization error at a speed of 60 km/h.
Deep Learning Models for the Prediction of Rainfall

Rainfall is one of the major source of freshwater for all the organism around the world. Rainfall prediction model provides the information regarding various climatological variables on the amount of rainfall. In recent days, Deep Learning enabled the self-learning data labels which allows to create a data-driven model for a time series dataset. It allows to make the anomaly/change detection from the time series data and also predicts the future event's data with respect to the events occurred in the past. This paper deals with obtaining models of the rainfall precipitation by using Deep Learning Architectures (LSTM and ConvNet) and determining the better architecture with RMSE of LSTM as 2.55 and RMSE of ConvNet as 2.44 claiming that for any time series dataset, Deep Learning models will be effective and efficient for the modellers.