nils-herrmann/clean_scirepeval huggingface dataset:
Smart traffic analytics in the semantic web with STAR-CITY: Scenarios, system and lessons learned in Dublin City

This paper gives a high-level presentation of STAR-CITY, a system supporting semantic traffic analytics and reasoning for city. STAR-CITY, which integrates (human and machine-based) sensor data using variety of formats, velocities and volumes, has been designed to provide insight on historical and real-time traffic conditions, all supporting efficient urban planning. Our system demonstrates how the severity of road traffic congestion can be smoothly analyzed, diagnosed, explored and predicted using semantic web technologies. Our prototype of semantics-aware traffic analytics and reasoning, illustrated and experimented in Dublin Ireland, but also tested in Bologna Italy, Miami USA and Rio Brazil works and scales efficiently with real, historical together with live and heterogeneous stream data. This paper highlights the lessons learned from deploying and using a system in Dublin City based on Semantic Web technologies.
Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting

The goal of traffic forecasting is to predict the future vital indicators (such as speed, volume and density) of the local traffic network in reasonable response time. Due to the dynamics and complexity of traffic network flow, typical simulation experiments and classic statistical methods cannot satisfy the requirements of mid-and-long term forecasting. In this work, we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Neural Network (STGCNN), to tackle this spatio-temporal sequence forecasting task. Instead of applying recurrent models to sequence learning, we build our model entirely on convolutional neural networks (CNNs) with gated linear units (GLU) and highway networks. The proposed architecture fully employs the graph structure of the road networks and enables faster training. Experiments show that our ST-GCNN network captures comprehensive spatio-temporal correlations throughout complex traffic network and consistently outperforms state-of-the-art baseline algorithms on several real-world traffic datasets.
Training with Confusion for Fine-Grained Visual Classification

Research in Fine-Grained Visual Classification has focused on tackling the variations in pose, lighting, and viewpoint using sophisticated localization and segmentation techniques, and the usage of robust texture features to improve performance. In this work, we look at the fundamental optimization of neural network training for fine-grained classification tasks with minimal inter-class variance, and attempt to learn features with increased generalization to prevent overfitting. We introduce Training-with-Confusion, an optimization procedure for fine-grained classification tasks that regularizes training by introducing confusion in activations. Our method can be generalized to any fine-tuning task; it is robust to the presence of small training sets and label noise; and adds no overhead to the prediction time. We find that Training-with-Confusion improves the state-of-the-art on all major fine-grained classification datasets.
Review on Crop Yield Prediction using Data Mining Focusing on Groundnut Crop and Naive Bayes Technique

In Indian economy, Agriculture is the most important branch and 70 percentage of rural population livelihood depends on agricultural work. Farming is the one of the important part of Agriculture. Crop yield depends on environment’s factors like precipitation, temperature, evapotranspiration, etc. Generally farmers cultivate crop, based on previous experience. But nowadays, the uncertainty increased in environment. So, accurate analysis of historic data of environment parameters should be done for successful farming. To get more harvest, we should also do the analysis of previous cultivation data. The Prediction of crop yield can be done based on historic crop cultivation data and weather data using data mining methods. This paper describes the role of data mining in Agriculture and crop yield prediction. This paper also describes Groundnut crop yield prediction analysis and Naive Bayes Method.
Six Weeks of Core Stability Training Improves Landing Kinetics Among Female Capoeira Athletes: A Pilot Study

Core stability training (CST) has increased in popularity among athletes and the general fitness population despite limited evidence CST programmes alone lead to improved athletic performance. In female athletes, neuromuscular training combining balance training and trunk and hip/pelvis dominant CST is suggested to reduce injury risk, and specifically peak vertical ground reaction forces (vGRF) in a drop jump landing task. However, the isolated effect of trunk dominant core stability training on vGRF during landing in female athletes had not been evaluated. Therefore, the objective of this study was to evaluate landing kinetics during a drop jump test following a CST intervention in female capoeira athletes. After giving their informed written consent, sixteen female capoeira athletes (mean ± SD age, stature, and body mass of 27.3 ± 3.7 years, 165.0 ± 4.0 cm, and 59.7 ± 6.3 kg, respectively) volunteered to participate in the training program which consisted of static and dynamic CST sessions, three times per week for six weeks. The repeated measures T-test revealed participants significantly reduced relative vGRF from pre- to post-intervention for the first (3.40 ± 0.78 vs. 2.85 ± 0.52 N·NBW-1, respectively [p<0.05, effect size = 0.60]), and second landing phase (5.09 ± 1.17 vs. 3.02 ± 0.41 N·NBW-1, respectively [p<0.001, effect size = 0.87]). The average loading rate was reduced from pre- to post-intervention during the second landing phase (30.96 ± 18.84 vs. 12.06 ± 9.83 N·NBW·s-1, respectively [p<0.01, effect size = 0.68]). The peak loading rate was reduced from pre- to post-intervention during the first (220.26 ± 111.51 vs. 120.27 ± 64.57 N·NBW·s-1 respectively [p<0.01, effect size = 0.64]), and second (99.52 ± 54.98 vs. 44.71 ± 30.34 N·NBW·s-1 respectively [p<0.01, effect size = 0.70]) landing phase. Body weight, average loading rate during the first landing phase, and jump height were not significantly different between week 0 and week 6 (p=0.528, p=0.261, and p=0.877, respectively). This study provides evidence that trunk dominant core stability training improves landing kinetics without improving jump height, and may reduce lower extremity injury risk in female athletes.
Implications of ethnic diversity

Ethnically differentiated societies are often regarded as dysfunctional, with poor economic performance and a high risk of violent civil conflict. I argue that this is not well-founded. I distinguish between dominance, in which one group constitutes a majority, and fractionalisation, in which there are many small groups. In terms of overall economic performance, I show that both theoretically and empirically, fractionalisation is normally unproblematic in democracies, although it can be damaging in dictatorships. Fractionalised societies have worse public sector performance, but this is offset by better private sector performance. Societies characterised by dominance are in principle likely to have worse economic performance, but empirically the effect is weak. In terms of the risk of civil war, I show that both theoretically and empirically fractionalisation actually makes societies safer, while dominance increases the risk of conflict. A policy implication is that fractionalised societies are viable and secession should be discouraged. P ub lic D is cl os ur e A ut ho riz ed
How active are K-12 students using Edmodo as online motivation, interaction and collaboration tools for learning process?

Edmodo called as ‘Facebook for schools’  enable the academic social network site connecting students and teachers anywhere and anytime. It can be used to encourage students to complete activities assigned by teachers such as assignment and quiz posting, polls conducting, badges awarding, learning materials uploading, classroom discussions, grade tracking, and comments writing besides as personal digital library connected to Google drive, OneDrive, and much more. Accordingly, this paper provided a general overview of how students in class IX to XII of technology information and communication course fully participate in Edmodo. The main beneficial point of this research was the trend and analysis of K-12 students' participation at high school in Indonesia. Investigating the participation and factors underlying the students' patterns of behavior in Edmodo can provide guidelines for sustainable improvement of online learning management systems. The data analysis revealed that a high number of students join as members in Edmodo and only a few of them were not engaged due to limited access of internet. It was found that most of students in class XII-IPA were active in posting their assignments through Edmodo after school and that most dominant is class X. There were some discussions and sharing information between teacher-students and students-students related to the inside and outside classroom activities. The result also indicated that students in class XII were more active than those in class IX, X, and XI. Students of class XII are in the final year before joining universities. Thus, they were more serious about their study. Furthermore, the students found Edmodo as a communication tool and a user-friendly social learning platform enabling them to connect with classmates and teachers in online class. The results also inspired teachers to motivate and encourage the students' to do their best in the study and build a strong relationship inside and outside the class.
Codebook Development for Team-Based Qualitative Analysis

One of the key elements in qualitative data analysis is the systematic coding of text (Strauss and Corbin 1990:57%60; Miles and Huberman 1994:56). Codes are the building blocks for theory or model building and the foundation on which the analyst’s arguments rest. Implicitly or explicitly, they embody the assumptions underlying the analysis. Given the context of the interdisciplinary nature of research at the Centers for Disease Control and Prevention (CDC), we have sought to develop explicit guidelines for all aspects of qualitative data analysis, including codebook development.
Instant effects of peppermint essential oil on the physiological parameters and exercise performance

Effect of peppermint on exercise performance was previously investigated but equivocal findings exist. This study aimed to investigate the effects of peppermint ingestion on the physiological parameters and exercise performance after 5 min and 1 h.



Thirty healthy male university students were randomly divided into experimental (n=15) and control (n=15) groups. Maximum isometric grip force, vertical and long jumps, spirometric parameters, visual and audio reaction times, blood pressure, heart rate, and breath rate were recorded three times: before, five minutes, and one hour after single dose oral administration of peppermint essential oil (50 µl). Data were analyzed using repeated measures ANOVA.



Our results revealed significant improvement in all of the variables after oral administration of peppermint essential oil. Experimental group compared with control group showed an incremental and a significant increase in the grip force (36.1%), standing vertical jump (7.0%), and standing long jump (6.4%). Data obtained from the experimental group after five minutes exhibited a significant increase in the forced vital capacity in first second (FVC1)(35.1%), peak inspiratory flow rate (PIF) (66.4%), and peak expiratory flow rate (PEF) (65.1%), whereas after one hour, only PIF shown a significant increase as compare with the baseline and control group. At both times, visual and audio reaction times were significantly decreased. Physiological parameters were also significantly improved after five minutes. A considerable enhancement in the grip force, spiromery, and other parameters were the important findings of this study.  An improvement in the spirometric measurements (FVC1, PEF, and PIF) might be due to the peppermint effects on the bronchial smooth muscle tonicity with or without affecting the lung surfactant. Yet, no scientific evidence exists regarding isometric force enhancement in this novel study.
Music in intervention for children and adolescents with autism: a meta-analysis.

This meta-analysis of 12 dependent variables from 9 quantitative studies comparing music to no-music conditions during treatment of children and adolescents with autism resulted in an overall effect size of d =.77 and a mean weighted correlation of r =.36 (p =.00). Since the confidence interval did not include 0, results were considered to be significant. All effects were in a positive direction, indicating benefits of the use of music in intervention. The homogeneity Q value was not significant (p =.83); therefore, results of included studies are considered to be homogeneous and explained by the overall effect size. The significant effect size, combined with the homogeneity of the studies, leads to the conclusion that all music intervention, regardless of purpose or implementation, has been effective for children and adolescents with autism. Included studies are described in terms of type of dependent variables measured; theoretical approach; number of subjects in treatment sessions; participation in and use, selection, and presentation of music; researcher discipline; published or unpublished source; and subject age. Clinical implications as well as recommendations for future research are discussed.
DeepFlow: Large Displacement Optical Flow with Deep Matching

Optical flow computation is a key component in many computer vision systems designed for tasks such as action detection or activity recognition. However, despite several major advances over the last decade, handling large displacement in optical flow remains an open problem. Inspired by the large displacement optical flow of Brox and Malik, our approach, termed Deep Flow, blends a matching algorithm with a variational approach for optical flow. We propose a descriptor matching algorithm, tailored to the optical flow problem, that allows to boost performance on fast motions. The matching algorithm builds upon a multi-stage architecture with 6 layers, interleaving convolutions and max-pooling, a construction akin to deep convolutional nets. Using dense sampling, it allows to efficiently retrieve quasi-dense correspondences, and enjoys a built-in smoothing effect on descriptors matches, a valuable asset for integration into an energy minimization framework for optical flow estimation. Deep Flow efficiently handles large displacements occurring in realistic videos, and shows competitive performance on optical flow benchmarks. Furthermore, it sets a new state-of-the-art on the MPI-Sintel dataset.
Continuous Online Sequence Learning with an Unsupervised Neural Network Model

The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory recently has been proposed as a theoretical framework for sequence learning in the cortex. In this letter, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variable order temporal sequences using an unsupervised Hebbian-like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory with other sequence learning algorithms, including statistical methods—autoregressive integrated moving average; feedforward neural networks—time delay neural network and online sequential extreme learning machine; and recurrent neural networks—long short-term memory and echo-state networks on sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for sequence learning, including continuous online learning, the ability to handle multiple predictions and branching sequences with high-order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyperparameter tuning. Therefore, the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem but is also applicable to real-world sequence learning problems from continuous data streams.
Deep Intrinsically Motivated Continuous Actor-Critic for Efficient Robotic Visuomotor Skill Learning

2019; 10:14–29  Deep intrinsically motivated continuous actor-critic for eflcient robotic visuomotor skill learning https://doi.org/10.1515/pjbr-2019-0005 Received June 6, 2018; accepted October 29, 2018  In this paper, we present a new intrinsically motivated actor-critic algorithm for learning continuous motor skills directly from raw visual input. Our neural architecture is composed of a critic and an actor network. Both networks receive the hidden representation of a deep convolutional autoencoder which is trained to reconstruct the visual input, while the centre-most hidden representation is also optimized to estimate the state value. Separately, an ensemble of predictive world models generates, based on its learning progress, an intrinsic reward signal which is combined with the extrinsic reward to guide the exploration of the actor-critic learner. Our approach is more data-efficient and inherently more stable than the existing actor-critic methods for continuous control from pixel data. We evaluate our algorithm for the task of learning robotic reaching and grasping skills on a realistic physics simulator and on a humanoid robot. The results show that the control policies learnedwith our approach can achieve better performance than the compared state-of-the-art and baseline algorithms in both dense-reward and challenging sparse-reward settings.
Proton magnetic resonance spectroscopy of cerebral lactate and other metabolites in stroke patients.

Proton magnetic resonance spectroscopy can measure in vivo brain lactate and other metabolites noninvasively. We measured the biochemical changes accompanying stroke in 16 human subjects with cortical or deep cerebral infarcts within the first 3 weeks after symptom onset, and performed follow-up studies on six.



One-dimensional proton spectroscopic imaging encompassing the infarct region was performed with a 2.1-T whole-body magnet using the stimulated echo pulse sequence and an echo time of 270 msec.



All but one of the cortical stroke patients had increased lactate within or near the infarct. Persistently elevated cerebral lactate was documented in five of six cases studied serially as long as 251 days after infarction. N-acetylaspartate levels were decreased in most cortical strokes. Elevated lactate, accompanied by minimal reduction in N-acetylaspartate, was recorded in two of four patients in the first week following a small subcortical infarct.



Long-term elevation of lactate commonly occurs after stroke. This lactate may arise from ongoing ischemia or infiltrating leukocytes, or it may be a residual of the lactate formed during the initial insult. The ability to observe stroke-elevated lactate pools at any time after lesion onset provides an approach to distinguishing among these possibilities in the future.
A partition-based method for string similarity joins with edit-distance constraints

As an essential operation in data cleaning, the similarity join has attracted considerable attention from the database community. In this article, we study string similarity joins with edit-distance constraints, which find similar string pairs from two large sets of strings whose edit distance is within a given threshold. Existing algorithms are efficient either for short strings or for long strings, and there is no algorithm that can efficiently and adaptively support both short strings and long strings. To address this problem, we propose a new filter, called the segment filter. We partition a string into a set of segments and use the segments as a filter to find similar string pairs. We first create inverted indices for the segments. Then for each string, we select some of its substrings, identify the selected substrings from the inverted indices, and take strings on the inverted lists of the found substrings as candidates of this string. Finally, we verify the candidates to generate the final answer. We devise efficient techniques to select substrings and prove that our method can minimize the number of selected substrings. We develop novel pruning techniques to efficiently verify the candidates. We also extend our techniques to support normalized edit distance. Experimental results show that our algorithms are efficient for both short strings and long strings, and outperform state-of-the-art methods on real-world datasets.
Contributions of Social Comparison and Self-Objectification in Mediating Associations Between Facebook Use and Emergent Adults' Psychological Well-Being

Although Facebook was created to help people feel connected with each other, data indicate that regular usage has both negative and positive connections to well-being. To explore these mixed results, we tested the role of social comparison and self-objectification as possible mediators of the link between Facebook use and three facets of psychological well-being: self-esteem, mental health, and body shame. Participants were 1,104 undergraduate women and men who completed surveys assessing their Facebook usage (minutes, passive use, and active use), social comparison, self-objectification, and well-being. Data were analyzed using structural equation modeling, testing separate models for women and men. Models for each gender fit the data well. For women and men, Facebook use was associated with greater social comparison and greater self-objectification, which, in turn, was each related to lower self-esteem, poorer mental health, and greater body shame. Mediated models provided better fits to the data than models testing direct pathways to the mediators and well-being variables. Implications are discussed for young people's social media use, and future directions are provided.
Modelling the Stock Market using

Stock markets are driven by a multitude of dynamics in which facts and beliefs play a major role in affecting the price of a company’s stock. In today’s information age, news can spread around the globe in some cases faster than they happen. While it can be beneficial for many applications including disaster prevention, our aim in this thesis is to use the timely release of information to model the stock market. We extract facts and beliefs from the population using one of the fastest growing social networking tools on the Internet, namely Twitter. We examine the use of Natural Language Processing techniques with a predictive machine learning approach to analyze millions of Twitter posts from which we draw distinctive features to create a model that enables the prediction of stock prices. We selected several stocks from the NASDAQ stock exchange and collected Intra-Day stock quotes during a period of two weeks. We build different feature representations from the raw Twitter posts and combined them with the stock price in order to build a regression model using the Support Vector Regression algorithm. We were able to build models of the stocks which predicted discrete prices that were close to a strong baseline. We further investigated the prediction of future prices, on average predicting 15 minutes ahead of the actual price, and evaluated the results using a Virtual Stock Trading Engine. These results were in general promising, but contained also some random variations across the different datasets.