bluuebunny/crossref_metadata_2025_split huggingface dataset:
Language dominance does not always predict cross-linguistic interactions in bilingual speech production
Authors: Amengual Mark , Simonet Miquel 
Published: July 2019

AbstractThe present study describes the results of two experiments that analyze the effects of language dominance on Catalan/Spanish bilingual speech production. We examined the acoustics of the Catalan [a]~[ə] alternation (a phonological process induced by lexical stress) and of the Catalan mid vowel contrasts /e/-/ɛ/ and /o/-/ɔ/ (two phonemic contrasts) in the speech of Catalan- vs. Spanish-dominant early Catalan/Spanish bilinguals from Majorca (Spain). The results indicate that, contrary to the analysis of the Catalan mid vowels, stressed and unstressed Spanish and Catalan /a/ yielded no significant difference as a function of language dominance. These findings suggest that unstressed vowel reduction, a phonological process, may be relatively easier to acquire than phonemic contrasts with a low functional load (/e/-/ɛ/, /o/-/ɔ/), perhaps because its predictability and high frequency may attract attention and/or relieve cognitive resources, which could be conducive to phonological learning. These findings demonstrate that language dominance effects in the production of phonemic contrasts, widely researched in the literature on early bilingualism, do not guarantee the same effects on the implementation of phonological processes.
Narrative stories, institutional rules, and the politics of pension policy in Canada and the United States
Authors: Béland Daniel 
Published: July 2019

ABSTRACTDeborah Stone’s Policy Paradox stresses the role of narrative stories in the construction of policy problems. This article takes a critical look at the Policy Paradox and at the claim that paying close attention to narratives is essential to the analysis of policy change. Drawing on recent scholarship on the role of ideas in public policy, the article shows that the analysis of narratives is part of a broader intellectual project associated with the ideational turn in policy analysis. The article explains how the analysis of narrative stories should take institutional factors into account, which can shape policy decisions. To illustrate these theoretical claims, the article analyzes the debate over public pension reform in the United States and Canada during the last 25 years. More specifically, the article compares the reform of the Canada Pension Plan during the mid-1990s and the failed push to privatize US Social Security during the Bill Clinton and the George W. Bush presidencies. The analysis stresses that, while narrative stories shape problem definition, institutional legacies influence policy adoption.
Regular changes in foot strike pattern during prolonged downhill running do not influence neuromuscular, energetics, or biomechanical parameters
Authors: Vernillo Gianluca , Aguiar Matheus , Savoldelli Aldo , Martinez Aaron , Giandolini Marlene , Horvais Nicolas , Edwards W. Brent , Millet Guillaume Y. 
Published: July 2019

AbstractResearch has suggested that a high variability in foot strike pattern during downhill running is associated with lower neuromuscular fatigue of the plantar flexors (PF). Given the popularity of trail running, we designed an intervention study to investigate whether a strategy with regular changes in foot strike pattern during downhill running could reduce the extent of fatigue on neuromuscular, energetics and biomechanical parameters as well as increase an uphill time‐to‐exhaustion trial (TTE) performance. Fourteen experienced trail runners completed two interventional conditions (separated by 15 days) in a pseudo‐randomised and counter‐balanced order that consisted of 2.5‐h of treadmill graded running with (switch condition) or without (control condition) a change between fore‐ and rear‐foot strike pattern every 30 s during the downhill sections. Pre and Post, neuromuscular tests were performed to assess PF central and peripheral fatigue. Energy cost of running was assessed using an indirect calorimetry system and biomechanical gait parameters were acquired with an instrumented treadmill. TTE was performed after both the graded running conditions. There were not significant condition × time interactions (p ≥ .085) for any of the variables considered, and TTE was not different between the two conditions (p = .755). A deliberate strategy to alternate between foot strike patterns did not reduce the extent of fatigue during prolonged graded running. We suggest that it is not the ability to switch between foot strike patterns that minimises fatigue; rather the ability to adapt foot strike pattern to the terrain and therefore a better running technique.
Assessment of the force‐velocity relationship during vertical jumps: influence of the starting position, analysis procedures and number of loads
Authors: Janicijevic Danica , Knezevic Olivera M. , Mirkov Dragan M. , Pérez‐Castilla Alejandro , Petrovic Milos , Samozino Pierre , Garcia‐Ramos Amador 
Published: July 2019

AbstractThis study aimed to compare the reliability and magnitude of the force‐velocity (F‐V) relationship parameters between the squat jumps performed from the 90° (SJ90) and self‐preferred knee angle (SJpref). A secondary aim was to explore the effect of the analysis procedure (force platform [FP] and Samozino's [SAM] method) and the number of loads tested (three‐ and two‐point methods) on the F‐V relationships. Twelve men were tested in two sessions during the SJ90 and SJpref. Two identical blocks of jumps were performed in each session against three external loads. The F‐V relationship parameters (maximum force, maximum velocity, F‐V slope and maximum power) were determined at each block through the FP and SAM procedures using the data collected under three (three‐point method) or only the two most distant loads (two‐point method). The average coefficient of variation (CV) of the four F‐V parameters revealed a higher reliability for the SJ90 compared to the SJpref (5.86% vs. 7.55%; CVratio = 1.29) with more pronounced differences using the FP (CVratio = 1.43) than the SAM procedure (CVratio = 1.14), and higher reliability for the SAM compared to the FP (6.14% vs. 7.27%; CVratio = 1.18). The SJpref and SAM procedures provided comparable or higher magnitude of the F‐V relationship parameters than the SJ90 and FP, respectively. The three‐ and two‐point methods revealed a comparable reliability and trivial differences in the magnitude of the F‐V relationship parameters. The routine testing procedure of the F‐V relationship could be simplified using the SJpref, the SAM procedure and the two‐point method.
The participation in organised sport doubles the odds of meeting physical activity recommendations in 7–12‐year‐old children
Authors: Mooses Kerli , Kull Merike 
Published: July 2019

AbstractThe purpose of this study was to determine the contributing role of organised sport participation to daily physical activity (PA) and to describe the PA during training. Children aged 7–12 wore the accelerometer for 1 week. Children with activity data for a minimum of 5 days were included in the analysis (n = 492). Evenson cut‐points were used to extract PA data for whole day and training sessions. Compliance with PA recommendations was assessed with two methods – weekly average and daily data. More than half (52.5%) of the children participated in an organised sport at least once a week. Participation in organised sport three or more times a week increased the odds of meeting PA recommendations (average method: OR = 4.02, 95% CI: 1.98–8.19, p < 0.001, days method: OR = 2.82, 95% CI: 1.35–5.88, p = 0.006). On average, children acquired 23.3 ± 15.3 min of moderate to vigorous PA (MVPA) during trainings, while boys accrued more MVPA minutes compared to girls (β = 5.2, 95% CI: 2.4–8.0) and the training time MVPA decreased with age (β = −1.0, 95% CI: −1.9 to −0.1), respectively. Each additional MVPA minute during training increased daily MVPA by 1.3 min (95% CI: 1.1–1.5). On days with training children accumulated 24.9 (95% CI: 21.8–28.0) more MVPA minutes compared to days without training. In conclusion, these findings highlight the importance of organised sport in supporting the PA levels of children. However, participation in the organised sport only is not sufficient to meet PA recommendations, and therefore, PA should be supported throughout the day.
Evolution of cooperation in synergistically evolving dynamic interdependent networks: fundamental advantages of coordinated network evolution
Authors: Yang Zhihu , Yu Changbin , Kim Jonghyuk , Li Zhi , Wang Long 
Published: July 2019

Abstract
Real networks are not only multi-layered yet also dynamic. The role of coordinated network evolution regarding dynamic multi-layer networks where both network and strategy evolution simultaneously show diverse interdependence by layers remains poorly addressed. Here, we propose a general and simple coevolution framework to analyze how coordination of different dynamical processes affects strategy propagation in synergistically evolving interdependent networks. The strategic feedback constitutes the main driving force of network evolution yet the inherent cross-layer self-optimization functions as its compensation. We show that these two ingredients often catalyze a better performance of network evolution in propagating cooperation. Coordinated network evolution may be a double-edged sword to cooperation and the network-adapting rate plays a crucial role in flipping its double-sided effect. It often economizes the cost and time consumption for driving the system to the full cooperation phase. Importantly, strongly coupled slow-tuned networks can outperform weakly coupled fast-regulated networks in solving social dilemmas, highlighting the fundamental advantages of coordinated network evolution and the importance of synergistic effect of dynamical processes in upholding human cooperation in multiplex networks.
A New Method for Abbreviation Prediction via CNN-BLSTM-CRF
Authors: Zheng Jianyu , Xiao Xinge , Wang Bihua , Zhu Yun , Yang Lijiao 
Published: July 2019

Abstract
It is a crucial problem to process abbreviation in the field of natural language processing. The most commonly used way to cope with this problem is to construct the reference database by predicting the abbreviation through its fully expanded form. Previous work on abbreviation prediction mostly rely on traditional machine learning algorithms, which inevitably requires a large number of manual annotations or expert knowledge to establish a feature system. In this paper, a neural network model based on CNN-BLSTM-CRF is proposed, which can predict Chinese abbreviations better without relying too much on the feature system: Firstly, convolutional neural network extracts phrase and Chinese character information from the fully expanded form, and then BLSTM-CRF deep network is constructed to annotate the fully expanded form, so as to extract its corresponding abbreviation form. The experimental results show that the method in this paper can perform better than the state-of-art method in traditional machine learning, and the results provide a reference for abbreviation research and the construction of resource repository.
Discrete Fireworks Algorithm for Welding Robot Path Planning
Authors: Zhou Xinyu , Zhao Qingjie , Zhang Dongxu 
Published: July 2019

Abstract
Welding robots are widely used in manufacturing industries, and reasonable welding path planning is an important issue in production efficiency. Welding robot path planning aims to arrange the sequence of weld joints and find the optimal welding path for the robot, which is essentially a combinatorial optimization problem. With the development of artificial intelligence, swarm intelligence algorithms shed new light on this problem. Fireworks algorithm (FWA) is a newly proposed swarm intelligence algorithm, simulating the process of fireworks explosion producing sparks to find the optimal solution. It has shown excellent performance in continuous optimization problems. In this paper, a discrete fireworks algorithm (DFWA), is proposed to solve the welding robot path planning problem. We introduce some operations to the framework of traditional FWA. In DFWA, 2-opt local search and crossover operator are applied on the explosion sparks. A new way of fireworks quality judgment is designed. Mutation operator is implemented for the generation of Gaussian sparks, and the selection strategy is improved. Simulation experiments have been made to verify our method and compare its performance with other current swarm intelligence algorithms. Experimental results show that the proposed method performs well with good convergence, stability and accuracy. It is effective for welding robot path planning.
Biologically Inspired Low Energy Clustering for Large Scale Wireless Sensor Networks
Authors: Zhou Jie , Xu Mengying , Lu Yi 
Published: July 2019

Abstract
The recent technological advances in data gathering, embedded micro-devices and mobile networking has significantly advanced the applications of small-size and numerous tiny nodes. Large scale wireless sensor networks (LSWSNs) are intensively studied and used in the fields of traffic avoidance, intelligent family, medical diagnostic, environmental, multimedia surveillance, military affairs and so on. The recent success of emerging LSWSNs technology has encouraged researchers to develop new low energy clustering algorithm in this field. In LSWSNs, reducing communication energy consumption of sensor will not lead to maximize network lifetime for the total system. The low energy clustering is a typical NP-hard combinatorial optimization problem. In this paper, an immune adaptive cuckoo search algorithm (IACSA) is given to reduce total energy consumption. We first design a fitness function to evaluate energy consumption of system. The IACSA is designed to improve the energy efficiency for LSWSNs. It has the advantages of immune generator that takes into account different benefits and adaptive operator to enhance the convergence rate. Simulations are conducted to show a comparison of IACSA with the shuffled frog leaping algorithm (SFLA), particle swarm optimization (PSO) and artificial fish swarm algorithm (AFSA). Results show that the proposed IACSA has lower energy consumption compared to the SFLA, AFSA and PSO, which means that the proposed method reduces the energy consumption.
Optimization of Low Power Wireless Sensor Network Lifetime Using Improved Clone Elite Monkey Algorithm
Authors: Lu Yi , Zhou Jie , Xu Mengying 
Published: July 2019

Abstract
Recent advances in efficient software algorithms, electronics and networking technologies have promoted the development of low computational complexity, low-cost, low storage, and intelligent tiny nodes. Low power wireless sensor networks (LPWSNs) are composed of some sensing units with limited communications as well as sensing capabilities. LPWSNs are approaches in a good deal of domains such as traffic control, automated assistance for the elderly monitors and so on. Recently network lifetime optimization has been receiving a lot of attention for wide applications of LPWSNs. Achieving a longer network lifetime under the restricted power source has a large calculation difficulty, which can be regard as an NP-hard problem. An improved clone elite monkey algorithm (ICEMA) to determine the network lifetime optimization in LPWSNs is given. In this paper, in order to achieve maximum network lifetime with full coverage, we first build a system model to calculate a better network lifetime. It is designed to increment the lifetime of the nodes for LPWSNs. The ICEMA has many advantages by combining the clone strategy as well as elite strategy. The simulations verify the robust and efficiency of ICEMA when compared with strategies based on evolutionary algorithm (EA), particle swarm algorithm (PSO) and artificial fish swarm algorithm (AFSA) under a LPWSNs conditions. The outcomes demonstrate that the proposed ICEMA can achieve a longer network lifetime than EA, PSO and AFSA while taking the same computational complexity.
Review Sentiment Orientation Analysis based on Deep Learning
Authors: Liu Yixuan , Xiong Hailing 
Published: July 2019

Abstract
The increase of network users has led to a large number of commentary languages on various network platforms. Traditional manual processing is time-consuming and labor-intensive. We need a mechanized way to process these commentary corpora and quickly uncover the emotional tendencies. A method of sentimental orientation analysis of comment text based on deep learning is proposed. First, we used GloVe model to train the word vector. Then, Give the different weight on word vector by using TF-IDF. Finally, the processed word vectors would be classificated by TextCNN. Experiments were carried out on the six categories of commodity review data crawled by Jingdong. This method can effectively identify the emotional tendency of the review text, which is more accurate than the traditional deep learning method.
Quasi-oppositional Multi-objective Antlion Optimizer Based on Differential Evolution
Authors: Yadong Wang , Quan Shi , Mingchang Song , Weixing Song 
Published: July 2019

Abstract
In order to solve the multi-objective problem, an improved quasi-oppositional multi-objective antlion optimization algorithm based on differential evolution (DEQOMALO) is proposed. This algorithm overcomes the defect that antlion algorithm is easy to fall into local optimum. On the one hand, this algorithm uses the idea of differential evolution to make full use of the information of the ant and the elite antlion to improve the position updating method of the original algorithm. On the other hand, the population is optimized by quasi-opposite learning strategy, and the original population and its quasi-opposite individuals are mixed and then selected as the new population, which greatly increases the diversity of the population. Finally, typical benchmarks are selected to compare the algorithm with the original antlion algorithm and other MALO algorithms with traditional evolution strategies. Experimental results show that both convergence and distribution of the improved algorithm are greatly improved. The proposed DEQOMALO algorithm has good adaptability and effectiveness in solving the two-objective optimization problem.
Facial-image based Age Estimation Using Imbalanced Datasets
Authors: Liu Yasi , Wang Tianzi , Zheng Limin , Yang Lu 
Published: July 2019

Abstract
Facial image based human age estimation is of great application significance. The public-available facial image datasets used for age estimation suffer greatly from the uneven distribution of images of different age groups, which may lead to the low estimation accuracy of the under-sampled age categories and limit the usage of the age estimation in certain applications. We propose a three-stage probability adjustment based CNN algorithm to solve the imbalanced distribution problem of the dataset. In particular, we construct an ENIN neural network structure by applying the Network in Network (NIN) structure to the traditional convolution neural network (CNN) and use the probability vector adjustment to improve the classification accuracy of the under-sampled age categories. Then, we filter out the images with high possibility of being misclassified after the probability vector adjustment and reset their categories by comparing cosine similarity and retraining the ensembled ENIN classifier. We also introduce a population-age-distribution based accuracy metric Accuracy-P to estimate the performance of the age estimation algorithm in real-world applications. Our experimental results confirm that our algorithm can effectively improve the overall estimation accuracy by significantly improving the accuracy of the under-sampled age groups while maintaining satisfactory accuracy for the other age groups.
An Approach to Edge Extraction Based on 3D Point Cloud for Robotic Chamfering
Authors: Wang Yongzhi , Du Zhijiang , Gao Yongzhuo , Li Mingyang , Dong Wei 
Published: July 2019

Abstract
Inconsistency of workpieces always results in low level automation of subsequent finishing process. As decreasing number of skilled workers, the demands of flexible manufacturing with robots is growing. In this paper, a kind of workpiece with complex free-form surface will be chamfered by industrial robots. Due to the intolerant differences among workpieces, traditional teach-playback method for robot is not appropriate for this application. The edge spline of each workpiece needs to be detected so that trajectories are generated to chamfer. Thus, an approach to edge extraction based on a 3D point cloud obtained by the 3D industrial camera is introduced to solve this problem. A time optimizing method is proposed to accelerate the extraction process. Finally, we decrease computing time from 55 minutes to 300 seconds, and the precision is 0.4180 mm.
New Research on Transfer Learning Model of Named Entity Recognition
Authors: Guan Guoliang , Zhu Min 
Published: July 2019

Abstract
This paper integrates the current Google’s most powerful NLP transfer learning model BERT with the traditional state-of-the-art BiLSTM-CRF model to solve the problem of named entity recognition. A bi-directional LSTM model can consider an effectively infinite amount of context on both sides of a word and eliminates the problem of limited context that applies to any feed-forward models. Google’s model applied a feedforward neural network, causing its performance to weaken. We seek to solve these issues by proposing a more powerful neural network model named BT-BiLSTM. The new neural network model has obtained F1 scores on three Chinese datasets exceeds the previous BiLSTM-CRF model, especially on the value of recall. It shows the great value of the combination of large scale none-labelled data pre-trained language model with named entity recognition, which inspire new ideas on other future work.
Detection of Non-Standard English Expressions by Language Sense
Authors: Chao Li , Jingxiang Zhang 
Published: July 2019

Abstract
This paper proposes an algorithm for perception English essay, in order to improve the ability of Chinese students’ writing which expressing more standard. Because students are influenced by their mother tongue in second language acquisition, English expression is not accurate enough. From the lexical and syntactic aspects, this paper uses the language sense relevance analysis algorithm (N-LanSen) based on the N-model to deeply perceive the students’ inappropriate expression in the essay. Different extraction methods are used to extract different features, and the corresponding feedback can be given to the places where the expression is not standard. The experimental results show that the algorithm can detect the place where English expression is not standard with high accuracy.
Study on Reactive Power Imbalance and Its solution of Parallel Coupled Transmission Lines
Authors: Li Dengfeng , Liu Yuming , Xiang Bin , Li Xiaoju , Si Meng , Yang Mincai 
Published: July 2019

Abstract
In view of the imbalance and even the abnormal phenomenon of reactive circulation of parallel high voltage transmission lines with similar measured parameters in actual operation, combined with field investigation and test, theoretical analysis and simulation demonstration, it is clear that this phenomenon is caused by the use of unconventional phase sequence and unreasonable transposition of the two-circuit lines when erecting the same tower. Based on the PSCAD simulation, the corresponding improvement measures are put forward from two aspects of optimizing the arrangement of phases and transposition. The results show that in order to solve the power imbalance problem of parallel coupled transmission lines, it is recommended to optimize the phase sequence of conductors in consideration of economy and operability.