bluuebunny/crossref_metadata_2025_split huggingface dataset:
Formal Psychological Assessment for Autism Spectrum Disorder Diagnosis: A New Methodology to Build An Adaptive Testing System
Authors: Pino Maria Chiara , Spoto Andrea , Mariano Melania , Granziol Umberto , Peretti Sara , Masedu Francesco , Valenti Marco , Mazza Monica , Vidotto Giulio 
Published: July 2018

Background:Early identification of Autism Spectrum Disorder (ASD) is of paramount importance in establishing effective interventions. Currently, the accepted gold standard for ASD diagnosis is the Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2).Methods:In our research study, we applied a novel approach to the ADOS-2 known as the Formal Psychological Assessment (FPA). The innovation of this methodology comes from the construction of a matrix that allows (1) the identification of the existing relations among items in terms of clinical symptoms and (2) an adaptive assessment of an individual.Objective:The aim of this study was to use a practical application of the FPA on the ADOS-2 and show its potential for psychological assessment of individuals with ASD. Particularly, this research raises some important considerations regarding the evaluation of this population and the authors propose future work to improve the quality of clinical assessment.Results:Our results suggest potential for the application of this new methodology to the ADOS-2 to support clinicians using diagnostic procedures.Conclusion:The use of FPA allows analysis of each item of ADOS’s modules based on the presence/absence of clinical elements. This system allows the identification of the critical areas for each individual and reduces the number of items required for the test.
Incipient Inter-Turn Short Circuit Fault Estimation Based on a Faulty Model Observer and ANN-Method for Induction Motor Drives
Authors: Bouakoura Mohamed , Naït-Saïd Mohamed-Said , Nait-Said Nasreddine 
Published: July 2018

Background:
According to statistics, short circuit faults are the second most frequent
faults in induction motors. Thus, in this paper, we investigated inter turn short circuit faults in their
early stage.

Methods:
A new equivalent model of the induction motor with turn to turn fault on one phase has
been developed. This model has been used to establish two schemes to estimate the severity of the
short circuit fault. In the first scheme, the faulty model is considered as an observer, where a correction
of an error between the measured and the estimated currents is the kernel of the fault severity
estimator. However, to develop the second method, the model was required only in the training process
of an artificial neural network (ANN). Since stator faults have a signature on symmetrical components
of phase currents, the magnitudes and angles of these components were used with the mean
speed value as inputs of the ANN. A simulation on MATLAB of both techniques has been performed
with various stator frequencies.

Results:
The suggested schemes prove a unique efficiency in the estimation of incipient turn to turn
fault. Besides, the ANN based scheme is less complex which reduces its implementation cost.

Conclusion:
To monitor the stator of an induction motor, the choice of the appropriate algorithm
should be done according to the system in which the motor will be installed. If the motor is directing
connected to the grid or fed via an inverter with a variable DC bus voltage, the observer would be
better, otherwise, the ANN algorithm is recommended.
Comparison between Seven MPPT Techniques Implemented in a Buck Converter
Authors: El Mentaly Lahcen , Amghar Abdellah , Sahsah Hassan 
Published: July 2018

Background:
The solar field on our planet is inexhaustible, which favors the use of photovoltaic
electricity which generates no nuisance: no greenhouse gases, no waste.

Methods:
It is a high value-added energy that is produced directly at the place of consumption
through photovoltaic (PV) solar panels. Notwithstanding these advantages, the maximum power depends
strongly on solar irradiation and temperature, which means that a Maximum Power Point
Tracking (MPPT) controller must be inserted between the PV panel and the load in order to follow
the Maximum Power Point (MPP) continuously and in real time. In this work, MPP’s behavior was
simulated at different temperatures and solar irradiations using seven techniques which identify the
MPP by different methods.

Results:
The novelty of this work is that the seven MPPT methods were compared according to a
very selective criterion which is the MPPT efficiency as well as a purely digital duty cycle control
without using the PI controller. The simulation under the PSIM software shows that the FLC, TP,
FSCC, TG, HC and IC methods have almost the same efficiency of 99%, whereas the FOCV method
had a low efficiency of 96%.

Conclusion:
This makes it possible to conclude that the best methods are FLC, HC and IC because
they use fewer sensors compared to the rest.
Hubungan Komunikasi Interpersonal Orang Tua dengan Perilaku Seksual Pranikah Remaja di Kota Padang
Authors: Putri Nita Tri , Dasuki Djaswadi , Wahyuni Budi 
Published: July 2018

Background: The phenomenon that is troubling many parties at the present time is the pattern of the adolescent courtship leading to sexual intercourse. Most teens consider premarital sexual behavior is normal and common. Interpersonal communication parents is not good to their children will cause conflicts that impact on adolescent premarital sexual behavior. By looking at the picture of interpersonal communication old man is expected to overcome adolescent premarital sexual behavior.Objektive : The purpose of this study was to determine the relationship between interpersonal communication of parents toward adolescent premarital sexual behavior.Method: This was an observational study using cross-sectional design and mixed methods approaches (quantitative and qualitative). The experiment was conducted at SMAN A, SMAN B, SMA C Idan MAN D Padang with research subjects totaling 299 teenagers. Informants for qualitative data that consists of 4 boys, 4 girls and 8 parents of the school to be studied.Results and Discussion: Indicates that there is a statistically significant relationship (p <0.05) between the independent variables (interpersonal communication of parents) with dependent variable (adolescent premarital sexual behavior) with a RP 3.71 95% CI (2.62 to 5.25). interpersonal communication of parents after the controlled variable peers, the media and religiosity there is also an increase of 49%.Conclusion: premarital sexual behavior in adolescents at risk because interpersonal communication of parents are not good . Other factors that affect the risk of premarital sexual behavior in adolescents are peers, the media and the level of religiosity. The results of in-depth interviews permissiveness and emotionally intimate relationship with parents also have an impact on the relationship is not good between parents and teenagers in interpersonal communication.Keywords: interpersonal communication of parents, adolescent premarital sexual behavior
Evaluasi Clinical Pathway Seksio Sesarea: Keefektifan Penggunaan Antibiotika Injeksi Cefotaxime 2 Gram dalam 24 Jam untuk Mencegah Terjadinya Infeksi Luka Operasi
Authors: Fauziah Rathi Manjari , Siswishanto Rukmono , Widad Shofwal 
Published: July 2018

Background: The usage of prophylactic antibiotics in Dr. Sardjito Hospital, Yogyakarta, is still diverse. Previously, prophylactic antibiotics that were given would be in a form of multidose and or multidrug regimen. Recently, a clinical pathway for c-section had been set up to uniform the antibiotics given. Cefotaime 2g, given intravenously, in 24 hours is the antibiotic of choice. Unfortunately, there was no data about the effetiveness of cefotaime that can be used as a basis of clinical pathway. Objective: Comparing the incidence of surgical site infection (SSI) between cefotaime 2g,/24 hours (clinical pathway or CP) with previous regimen of prophylactic antibiotics (non-clinical pathway or nonCP) and also identify the risk factors.Method: This is a retrospective cohort study with 129 subjects, divided into two groups. The CP group consists of 63 subjects, while non-CP group consists of 66 subjects. The surgical site infection was observed in the day 3 and day 10 aer C-section. Multivariat analysis was used to determine the risk factors of SSI.Result and Discussion: SSI incidence in the CP group at day 3 was higher compared to non-CP group, but it was not statistically significant (OR 4,73 95% CI 0,52 43,04), eukocytosis (>17000/mcl) was the independent risk factor for SSI (OR 7,54 95% CI 1,25 45,39).Conclusion: SSI incidence between two groups was not statistically significant but was clinically significant. The presence of leukocytosis is becoming the risk factor for SSI.Keywords: prophylactic antibiotic, c-section, cesarean section, surgical site infectio
Diachronic trends in the evolution of civilizations in the Nile valley
Authors: Кормышева Элеонора , Kormysheva Eleonora 
Published: July 2018

The diachronic trends in socio-economic and cultural development of the societies in the Nile valley are revealed based on the materials from Giza necropolis (the 3rd millennium BC) and the settlement of Abu Erteila (1st century AD). The research made it possible to trace the typological similarities in the evolution of the studied societies in cultural and historical contexts.
The main fields of the research were epigraphy, iconography, social history, and material culture.
Many previously unknown monuments discovered by Russian archaeologists in Egypt and Sudan were introduced into scientific discourse. The basis was created for studying the Nile valley as a contact zone between the Mediterranean world and Africa.
IDENTIDADE, REFERENCIAÇÃO E TEXTO DIGITAL: UMA ANÁLISE LINGUÍSTICO-DISCURSIVA
Authors: Aranha Marize Barros Rocha , Cardoso Evaldo Carlos De Oliveira 
Published: July 2018

RESUMO: Ao articularmos os pressupostos teóricos da referenciação à questão da identidade e texto digital, mostraremos que essa tríplice relação é possível numa perspectiva de análise linguística e discursiva. Para tanto, fazemos uma breve abordagem sobre a trajetória evolutiva da Linguística Textual, com foco na referenciação, processo fundamental para a construção do sentido do texto; discutimos acerca da identidade do sujeito, a partir dos processos referenciais, enfatizando a construção dos objetos de discurso; também discorremos sobre o texto digital e suas características, centrando-nos na rede social Facebook como espaço de escrita do texto digital. O aporte teórico é constituído pelos seguintes autores: Araújo e Leffa (2016), Antunes (2010 e 2017), Barton e Lee (2015), Cavalcante (2003, 2012, 2013 e 2014), Charaudeau (2012), Hall (1999), Koch (2010, 2014, 2015 e 2017), Martins (2016), Mondada e Dubois (2016), Roncarati (2010), Shepherd e Saliés (2013) e Silva (2007).Palavras-chave: Identidade; Referenciação; Texto digital; Discurso.
REFERENCIAÇÃO E CONSTRUÇÃO DE IDENTIDADE FEMININA NO TEXTO PUBLICITÁRIO DA NATURA
Authors: Faria Maria da Graça Dos Santos , Araujo Keila de Jesus Ribeiro 
Published: July 2018

Esse trabalho tem como objetivo analisar como os processos de referenciação ajudam a construir as identidades social e discursiva da mulher em textos da publicidade brasileira. Neste caso, investigaremos a relação entre Referenciação e Identidade em um texto publicitário veiculado pela Natura. Para tal, utilizaremos as contribuições de Hall (2006) e Silva (2000) sobre Identidade; os estudos de Cavalcante (2011, 2014), Capistrano Júnior (2017) e Ciula (2008) da área da Linguística Textual, voltados para a Referenciação, e o texto de Sandmann (2007) relacionado à publicidade. A análise será realizada a partir de uma propaganda da Natura, de outubro de 2016 e divulgada na Internet, em que destacaremos os processos referenciais – anafóricos e dêiticos, que auxiliam na construção dos referentes e da identidade. PALAVRAS-CHAVE: Identidade; Referenciação; Publicidade.
PARADIGM DECONSTRUCTION OF JEJAK DEDARI YANG MENARI DI ANTARA MITOS DAN KARMA A NOVEL BY ERWIN ARNADA
Authors: Pratiwi Paramita Nur , Suyitno Suyitno , Wardani Nugraheni Eko 
Published: July 2018

This paper aim to describe paradigm deconstruction of previous basic thought about Balinese myth in a Jejak Dedari yang Menari di Antara Mitos dan Karma, a novel by Erwin Arnada. This paper is qualitative descriptive. This paper using content analysis method with deconstruction literacy approach. The text of Jejak Dedari yang Menari di Antara Mitos dan Karma a novel by Erwin Arnada was used as source of paper data that published in 2016 by Gagas Media. Data collection techniques used in this paper are reading, record and data card techniques. The data validity used triangulacy theory. The result in this paper are (1) Myths of Kolok children, and (2) Sincerity and taksu bring the new soul. Both results obtained reveal that the belief in something can be overturned so that what is commonly considered as a belief can be dismantled into taboos or vice versa.Keyword: Deconstruction, Myth, Novel, Erwin ArnadaDEKONSTRUKSI PARADIGMA DALAM NOVEL JEJAK DEDARI YANG MENARI DI ANTARA MITOS DAN KARMA KARYA ERWIN ARNADAAbstrakPenelitian ini bertujuan untuk mendeskripsikan dekonstruksi paradigma tentang dasar pemikiran terdahulu terhadap suatu mitos yang berada di Bali dalam novel Jejak Dedari yang Menari di Antara Mitos dan Karma karya Erwin Arnada. Penelitian ini berbentuk deskriptif kualitatif. Metode yang digunakan adalah metode content analysis dengan pendekatan dekonstruksi sastra. Sumber data penelitian ini adalah teks novel yang berjudul Jejak Dedari yang Menari di Antara Mitos dan Karma karya Erwin Arnada yang dirilis pada tahun 2016 produksi GagasMedia. Teknik pengumpulan data dalam penelitian ini adalah teknik baca, catat dan kartu data. Validitas data menggunakan triangulasi teori. Hasil penelitian ini memaparkan, (1) Mitos anak kolok, dan (2) Ketulusan dan taksu menghadirkan jiwa yang baru. Kedua hasil yang didapat mengungkapkan bahwa kepercayaan terhadap sesuatu dapat dijungkirbalikan sehingga hal yang biasanya dianggap lumrah dan sudah menjadi keyakinan dapat dibongkar menjadi hal yang tabu atau sebaliknya.Kata Kunci: Dekonstruksi, Mitos, Novel, Erwin Arnada
Dilated Convolutional Network with Iterative Optimization for Continuous Sign Language Recognition
Authors: Pu Junfu , Zhou Wengang , Li Houqiang 
Published: July 2018

This paper presents a novel deep neural architecture with iterative optimization strategy for real-world continuous sign language recognition. Generally, a continuous sign language recognition system consists of visual input encoder for feature extraction and a sequence learning model to learn the correspondence between the input sequence and the output sentence-level labels. We use a 3D residual convolutional network (3D-ResNet) to extract visual features. After that, a stacked dilated convolutional network with Connectionist Temporal Classification (CTC) is applied for learning the mapping between the sequential features and the text sentence. The deep network is hard to train since the CTC loss has limited contribution to early CNN parameters. To alleviate this problem, we design an iterative optimization strategy to train our architecture. We generate pseudo-labels for video clips from sequence learning model with CTC, and fine-tune the 3D-ResNet with the supervision of pseudo-labels for a better feature representation. We alternately optimize feature extractor and sequence learning model with iterative steps. Experimental results on RWTH-PHOENIX-Weather, a large real-world continuous sign language recognition benchmark, demonstrate the advantages and effectiveness of our proposed method.
Probabilistic bipolar abstract argumentation frameworks: complexity results
Authors: Fazzinga Bettina , Flesca Sergio , Furfaro Filippo 
Published: July 2018

Probabilistic Bipolar Abstract Argumentation Frameworks (prBAFs), combining the possibility of specifying supports between arguments with a probabilistic modeling of the uncertainty, are considered, and the complexity of the fundamentalproblem of computing extensions' probabilities is addressed.The most popular semantics of supports and extensions are considered, as well as different paradigms for defining the probabilistic encoding of the uncertainty.Interestingly, the presence of supports, which does not alter the complexity of verifying extensions in the deterministic case, is shown to introduce a new source of complexity in some probabilistic settings, for which tractable cases are also identified.
Regularizing Deep Neural Networks with an Ensemble-based Decorrelation Method
Authors: Gu Shuqin , Hou Yuexian , Zhang Lipeng , Zhang Yazhou 
Published: July 2018

Although Deep Neural Networks (DNNs) have achieved excellent performance in many tasks, improving the generalization capacity of DNNs still remains a challenge. In this work, we propose a novel regularizer named Ensemble-based Decorrelation Method (EDM), which is motivated by the idea of the ensemble learning to improve generalization capacity of DNNs. EDM can be applied to hidden layers in fully connected neural networks or convolutional neural networks. We treat each hidden layer as an ensemble of several base learners through dividing all the hidden units into several non-overlap groups, and each group will be viewed as a base learner. EDM encourages DNNs to learn more diverse representations by minimizing the covariance between all base learners during the training step. Experimental results on MNIST and CIFAR datasets demonstrate that EDM can effectively reduce the overfitting and improve the generalization capacity of DNNs
Doubly Aligned Incomplete Multi-view Clustering
Authors: Hu Menglei , Chen Songcan 
Published: July 2018

Nowadays, multi-view clustering has attracted more and more attention. To date, almost all the previous studies assume that views are complete. However, in reality, it is often the case that each view may contain some missing instances. Such incompleteness makes it impossible to directly use traditional multi-view clustering methods. In this paper, we propose a Doubly Aligned Incomplete Multi-view Clustering algorithm (DAIMC) based on weighted semi-nonnegative matrix factorization (semi-NMF). Specifically, on the one hand, DAIMC utilizes the given instance alignment information to learn a common latent feature matrix for all the views. On the other hand, DAIMC establishes a consensus basis matrix with the help of L2,1-Norm regularized regression for reducing the influence of missing instances. Consequently, compared with existing methods, besides inheriting the strength of semi-NMF with ability to handle negative entries, DAIMC has two unique advantages: 1) solving the incomplete view problem by introducing a respective weight matrix for each view, making it able to easily adapt to the case with more than two views; 2) reducing the influence of view incompleteness on clustering by enforcing the basis matrices of individual views being aligned with the help of regression. Experiments on four real-world datasets demonstrate its advantages.
Structured Inference for Recurrent Hidden Semi-markov Model
Authors: Liu Hao , He Lirong , Bai Haoli , Dai Bo , Bai Kun , Xu Zenglin 
Published: July 2018

Segmentation and labeling for high dimensional time series is an important yet challenging task in a number of applications, such as behavior understanding and medical diagnosis. Recent advances to model the nonlinear dynamics in such time series data, has suggested to involve recurrent neural networks into Hidden Markov Models. However, this involvement has caused the inference procedure much more complicated, often leading to intractable inference, especially for the discrete variables of segmentation and labeling. To achieve both flexibility and tractability in modeling nonlinear dynamics of discrete variables, we present a structured and stochastic sequential neural network (SSNN), which composes with a generative network and an inference network. In detail, the generative network aims to not only capture the long-term dependencies but also model the uncertainty of the segmentation labels via semi-Markov models. More importantly, for efficient and accurate inference, the proposed bi-directional inference network reparameterizes the categorical segmentation with the Gumbel-Softmax approximation and resorts to the Stochastic Gradient Variational Bayes. We evaluate the proposed model in a number of tasks, including speech modeling, automatic segmentation and labeling in behavior understanding, and sequential multi-objects recognition. Experimental results have demonstrated that our proposed model can achieve significant improvement over the state-of-the-art methods.
Extracting Job Title Hierarchy from Career Trajectories: A Bayesian Perspective
Authors: Xu Huang , Yu Zhiwen , Guo Bin , Teng Mingfei , Xiong Hui 
Published: July 2018

A job title usually implies the responsibility and the rank of a job position. While traditional job title analysis has been focused on studying the responsibilities of job titles, this paper attempts to reveal the rank of job titles. Specifically, we propose to extract job title hierarchy from employees' career trajectories. Along this line, we first quantify the Difficulty of Promotion (DOP) from one job title to another by a monotonic transformation of the length of tenure based on the assumption that a longer tenure usually implies a greater difficulty to be promoted. Then, the difference of two job title ranks is defined as a mapping of the DOP observed from job transitions. A Gaussian Bayesian Network (GBN) is adopted to model the joint distribution of the job title ranks and the DOPs in a career trajectory. Furthermore, a stochastic algorithm is developed for inferring the posterior job title rank by a given collection of DOPs in the GBN. Finally, experiments on more than 20 million job trajectories show that the job title hierarchy can be extracted precisely by the proposed method.
Goal-Oriented Chatbot Dialog Management Bootstrapping with Transfer Learning
Authors: Ilievski Vladimir , Musat Claudiu , Hossman Andreea , Baeriswyl Michael 
Published: July 2018

Goal-Oriented (GO) Dialogue Systems, colloquially known as goal oriented chatbots, help users achieve a predefined goal (e.g. book a movie ticket) within a closed domain. A first step is to understand the user's goal by using natural language understanding techniques. Once the goal is known, the bot must manage a dialogue to achieve that goal, which is conducted with respect to a learnt policy. The success of the dialogue system depends on the quality of the policy, which is in turn reliant on the availability of high-quality training data for the policy learning method, for instance Deep Reinforcement Learning. Due to the domain specificity, the amount of available data is typically too low to allow the training of good dialogue policies. In this paper we introduce a transfer learning method to mitigate the effects of the low in-domain data availability. Our transfer learning based approach improves the bot's success rate by 20% in relative terms for distant domains and we more than double it for close domains, compared to the model without transfer learning. Moreover, the transfer learning chatbots learn the policy up to 5 to 10 times faster. Finally, as the transfer learning approach is complementary to additional processing such as warm-starting, we show that their joint application gives the best outcomes.