bluuebunny/crossref_metadata_2025 huggingface dataset:
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data
Authors: Zhang Chuxu , Song Dongjin , Chen Yuncong , Feng Xinyang , Lumezanu Cristian , Cheng Wei , Ni Jingchao , Zong Bo , Chen Haifeng , Chawla Nitesh V.
Published: September 2019

Nowadays, multivariate time series data are increasingly collected in various real world systems, e.g., power plants, wearable devices, etc. Anomaly detection and diagnosis in multivariate time series refer to identifying abnormal status in certain time steps and pinpointing the root causes. Building such a system, however, is challenging since it not only requires to capture the temporal dependency in each time series, but also need encode the inter-correlations between different pairs of time series. In addition, the system should be robust to noise and provide operators with different levels of anomaly scores based upon the severity of different incidents. Despite the fact that a number of unsupervised anomaly detection algorithms have been developed, few of them can jointly address these challenges. In this paper, we propose a Multi-Scale Convolutional Recurrent Encoder-Decoder (MSCRED), to perform anomaly detection and diagnosis in multivariate time series data. Specifically, MSCRED first constructs multi-scale (resolution) signature matrices to characterize multiple levels of the system statuses in different time steps. Subsequently, given the signature matrices, a convolutional encoder is employed to encode the inter-sensor (time series) correlations and an attention based Convolutional Long-Short Term Memory (ConvLSTM) network is developed to capture the temporal patterns. Finally, based upon the feature maps which encode the inter-sensor correlations and temporal information, a convolutional decoder is used to reconstruct the input signature matrices and the residual signature matrices are further utilized to detect and diagnose anomalies. Extensive empirical studies based on a synthetic dataset and a real power plant dataset demonstrate that MSCRED can outperform state-ofthe-art baseline methods.
Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making
Authors: Aghaei Sina , Azizi Mohammad Javad , Vayanos Phebe
Published: September 2019

In recent years, automated data-driven decision-making systems have enjoyed a tremendous success in a variety of fields (e.g., to make product recommendations, or to guide the production of entertainment). More recently, these algorithms are increasingly being used to assist socially sensitive decisionmaking (e.g., to decide who to admit into a degree program or to prioritize individuals for public housing). Yet, these automated tools may result in discriminative decision-making in the sense that they may treat individuals unfairly or unequally based on membership to a category or a minority, resulting in disparate treatment or disparate impact and violating both moral and ethical standards. This may happen when the training dataset is itself biased (e.g., if individuals belonging to a particular group have historically been discriminated upon). However, it may also happen when the training dataset is unbiased, if the errors made by the system affect individuals belonging to a category or minority differently (e.g., if misclassification rates for Blacks are higher than for Whites). In this paper, we unify the definitions of unfairness across classification and regression. We propose a versatile mixed-integer optimization framework for learning optimal and fair decision trees and variants thereof to prevent disparate treatment and/or disparate impact as appropriate. This translates to a flexible schema for designing fair and interpretable policies suitable for socially sensitive decision-making. We conduct extensive computational studies that show that our framework improves the state-of-the-art in the field (which typically relies on heuristics) to yield non-discriminative decisions at lower cost to overall accuracy.
Exploiting Background Knowledge in Compact Answer Generation for Why-Questions
Authors: Iida Ryu , Kruengkrai Canasai , Ishida Ryo , Torisawa Kentaro , Oh Jong-Hoon , Kloetzer Julien
Published: September 2019

This paper proposes a novel method for generating compact answers to open-domain why-questions, such as the following answer, “Because deep learning technologies were introduced,” to the question, “Why did Google’s machine translation service improve so drastically?” Although many works have dealt with why-question answering, most have focused on retrieving as answers relatively long text passages that consist of several sentences. Because of their length, such passages are not appropriate to be read aloud by spoken dialog systems and smart speakers; hence, we need to create a method that generates compact answers. We developed a novel neural summarizer for this compact answer generation task. It combines a recurrent neural network-based encoderdecoder model with stacked convolutional neural networks and was designed to effectively exploit background knowledge, in this case a set of causal relations (e.g., “[Microsoft’s machine translation has made great progress over the last few years]effect since [it started to use deep learning.]cause”) that was extracted from a large web data archive (4 billion web pages). Our experimental results show that our method achieved significantly better ROUGE F-scores than existing encoder-decoder models and their variations that were augmented with query-attention and memory networks, which are used to exploit the background knowledge.
A SAT+CAS Approach to Finding Good Matrices: New Examples and Counterexamples
Authors: Bright Curtis , Ðoković Dragomir Ž. , Kotsireas Ilias , Ganesh Vijay
Published: September 2019

We enumerate all circulant good matrices with odd orders divisible by 3 up to order 70. As a consequence of this we find a previously overlooked set of good matrices of order 27 and a new set of good matrices of order 57. We also find that circulant good matrices do not exist in the orders 51, 63, and 69, thereby finding three new counterexamples to the conjecture that such matrices exist in all odd orders. Additionally, we prove a new relationship between the entries of good matrices and exploit this relationship in our enumeration algorithm. Our method applies the SAT+CAS paradigm of combining computer algebra functionality with modern SAT solvers to efficiently search large spaces which are specified by both algebraic and logical constraints.
Improving Optimization Bounds Using Machine Learning: Decision Diagrams Meet Deep Reinforcement Learning
Authors: Cappart Quentin , Goutierre Emmanuel , Bergman David , Rousseau Louis-Martin
Published: September 2019

Finding tight bounds on the optimal solution is a critical element of practical solution methods for discrete optimization problems. In the last decade, decision diagrams (DDs) have brought a new perspective on obtaining upper and lower bounds that can be significantly better than classical bounding mechanisms, such as linear relaxations. It is well known that the quality of the bounds achieved through this flexible bounding method is highly reliant on the ordering of variables chosen for building the diagram, and finding an ordering that optimizes standard metrics is an NP-hard problem. In this paper, we propose an innovative and generic approach based on deep reinforcement learning for obtaining an ordering for tightening the bounds obtained with relaxed and restricted DDs. We apply the approach to both the Maximum Independent Set Problem and the Maximum Cut Problem. Experimental results on synthetic instances show that the deep reinforcement learning approach, by achieving tighter objective function bounds, generally outperforms ordering methods commonly used in the literature when the distribution of instances is known. To the best knowledge of the authors, this is the first paper to apply machine learning to directly improve relaxation bounds obtained by general-purpose bounding mechanisms for combinatorial optimization problems.
Model-Based Diagnosis of Hybrid Systems Using Satisfiability Modulo Theory
Authors: Diedrich Alexander , Maier Alexander , Niggemann Oliver
Published: September 2019

Currently, detecting and isolating faults in hybrid systems is often done manually with the help of human operators. In this paper we present a novel model-based diagnosis approach for automatically diagnosing hybrid systems. The approach has two parts: First, modelling dynamic system behaviour is done through well-known state space models using differential equations. Second, from the state space models we calculate Boolean residuals through an observer-pattern. The novelty lies in implementing the observer pattern through the use of a symbolic system description specified in satisfiability theory modulo linear arithmetic. With this, we create a static situation for the diagnosis algorithm and decouple modelling and diagnosis. Evaluating the system description generates one Boolean residual for each component. These residuals constitute the fault symptoms. To find the minimum cardinality diagnosis from these symptoms we employ Reiter’s diagnosis lattice.For the experimental evaluation we use a simulation of the Tennessee Eastman process and a simulation of a four-tank model. We show that the presented approach is able to identify all injected faults.
Graph Convolutional Networks Meet Markov Random Fields: Semi-Supervised Community Detection in Attribute Networks
Authors: Jin Di , Liu Ziyang , Li Weihao , He Dongxiao , Zhang Weixiong
Published: September 2019

Community detection is a fundamental problem in network science with various applications. The problem has attracted much attention and many approaches have been proposed. Among the existing approaches are the latest methods based on Graph Convolutional Networks (GCN) and on statistical modeling of Markov Random Fields (MRF). Here, we propose to integrate the techniques of GCN and MRF to solve the problem of semi-supervised community detection in attributed networks with semantic information. Our new method takes advantage of salient features of GNN and MRF and exploits both network topology and node semantic information in a complete end-to-end deep network architecture. Our extensive experiments demonstrate the superior performance of the new method over state-of-the-art methods and its scalability on several large benchmark problems.
Generalized Batch Normalization: Towards Accelerating Deep Neural Networks
Authors: Yuan Xiaoyong , Feng Zheng , Norton Matthew , Li Xiaolin
Published: September 2019

Utilizing recently introduced concepts from statistics and quantitative risk management, we present a general variant of Batch Normalization (BN) that offers accelerated convergence of Neural Network training compared to conventional BN. In general, we show that mean and standard deviation are not always the most appropriate choice for the centering and scaling procedure within the BN transformation, particularly if ReLU follows the normalization step. We present a Generalized Batch Normalization (GBN) transformation, which can utilize a variety of alternative deviation measures for scaling and statistics for centering, choices which naturally arise from the theory of generalized deviation measures and risk theory in general. When used in conjunction with the ReLU non-linearity, the underlying risk theory suggests natural, arguably optimal choices for the deviation measure and statistic. Utilizing the suggested deviation measure and statistic, we show experimentally that training is accelerated more so than with conventional BN, often with improved error rate as well. Overall, we propose a more flexible BN transformation supported by a complimentary theoretical framework that can potentially guide design choices.
Generalized Distance Bribery
Authors: Baumeister Dorothea , Hogrebe Tobias , Rey Lisa
Published: September 2019

The bribery problem in elections asks whether an external agent can make some distinguished candidate win or prevent her from winning, by bribing some of the voters. This problem was studied with respect to the weighted swap distance between two votes by Elkind et al. (2009). We generalize this definition by introducing a bound on the distance between the original and the bribed votes. The distance measures we consider include a restriction of the weighted swap distance and variants of the footrule distance, which capture some realworld models of influence an external agent may have on the voters. We study constructive and destructive variants of distance bribery for scoring rules and obtain polynomial-time algorithms as well as NP-hardness results. For the case of element-weighted swap and element-weighted footrule distances, we give a complete dichotomy result for the class of pure scoring rules.
Context-Aware Self-Attention Networks
Authors: Yang Baosong , Li Jian , Wong Derek F. , Chao Lidia S. , Wang Xing , Tu Zhaopeng
Published: September 2019

Self-attention model has shown its flexibility in parallel computation and the effectiveness on modeling both long- and short-term dependencies. However, it calculates the dependencies between representations without considering the contextual information, which has proven useful for modeling dependencies among neural representations in various natural language tasks. In this work, we focus on improving self-attention networks through capturing the richness of context. To maintain the simplicity and flexibility of the self-attention networks, we propose to contextualize the transformations of the query and key layers, which are used to calculate the relevance between elements. Specifically, we leverage the internal representations that embed both global and deep contexts, thus avoid relying on external resources. Experimental results on WMT14 English⇒German and WMT17 Chinese⇒English translation tasks demonstrate the effectiveness and universality of the proposed methods. Furthermore, we conducted extensive analyses to quantify how the context vectors participate in the self-attention model.
Evolutionarily Learning Multi-Aspect Interactions and Influences from Network Structure and Node Content
Authors: Jian Songlei , Hu Liang , Cao Longbing , Lu Kai , Gao Hang
Published: September 2019

The formation of a complex network is highly driven by multi-aspect node influences and interactions, reflected on network structures and the content embodied in network nodes. Limited work has jointly modeled all these aspects, which typically focuses on topological structures but overlooks the heterogeneous interactions behind node linkage and contributions of node content to the interactive heterogeneities. Here, we propose a multi-aspect interaction and influence-unified evolutionary coupled system (MAI-ECS) for network representation by involving node content and linkage-based network structure. MAI-ECS jointly and iteratively learns two systems: a multi-aspect interaction learning system to capture heterogeneous hidden interactions between nodes and an influence propagation system to capture multiaspect node influences and their propagation between nodes. MAI-ECS couples, unifies and optimizes the two systems toward an effective representation of explicit node content and network structure, and implicit node interactions and influences. MAI-ECS shows superior performance in node classification and link prediction in comparison with the stateof-the-art methods on two real-world datasets. Further, we demonstrate the semantic interpretability of the results generated by MAI-ECS.
Allocating Interventions Based on Predicted Outcomes: A Case Study on Homelessness Services
Authors: Kube Amanda , Das Sanmay , Fowler Patrick J.
Published: September 2019

Modern statistical and machine learning methods are increasingly capable of modeling individual or personalized treatment effects. These predictions could be used to allocate different interventions across populations based on individual characteristics. In many domains, like social services, the availability of different possible interventions can be severely resource limited. This paper considers possible improvements to the allocation of such services in the context of homelessness service provision in a major metropolitan area. Using data from the homeless system, we use a counterfactual approach to show potential for substantial benefits in terms of reducing the number of families who experience repeat episodes of homelessness by choosing optimal allocations (based on predicted outcomes) to a fixed number of beds in different types of homelessness service facilities. Such changes in the allocation mechanism would not be without tradeoffs, however; a significant fraction of households are predicted to have a higher probability of re-entry in the optimal allocation than in the original one. We discuss the efficiency, equity and fairness issues that arise and consider potential implications for policy.
Bidirectional Inference Networks:A Class of Deep Bayesian Networks for Health Profiling
Authors: Wang Hao , Mao Chengzhi , He Hao , Zhao Mingmin , Jaakkola Tommi S. , Katabi Dina
Published: September 2019

We consider the problem of inferring the values of an arbitrary set of variables (e.g., risk of diseases) given other observed variables (e.g., symptoms and diagnosed diseases) and high-dimensional signals (e.g., MRI images or EEG). This is a common problem in healthcare since variables of interest often differ for different patients. Existing methods including Bayesian networks and structured prediction either do not incorporate high-dimensional signals or fail to model conditional dependencies among variables. To address these issues, we propose bidirectional inference networks (BIN), which stich together multiple probabilistic neural networks, each modeling a conditional dependency. Predictions are then made via iteratively updating variables using backpropagation (BP) to maximize corresponding posterior probability. Furthermore, we extend BIN to composite BIN (CBIN), which involves the iterative prediction process in the training stage and improves both accuracy and computational efficiency by adaptively smoothing the optimization landscape. Experiments on synthetic and real-world datasets (a sleep study and a dermatology dataset) show that CBIN is a single model that can achieve state-of-the-art performance and obtain better accuracy in most inference tasks than multiple models each specifically trained for a different task.
Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems
Authors: Fu Wenjing , Peng Zhaohui , Wang Senzhang , Xu Yang , Li Jin
Published: September 2019

As one promising way to solve the challenging issues of data sparsity and cold start in recommender systems, crossdomain recommendation has gained increasing research interest recently. Cross-domain recommendation aims to improve the recommendation performance by means of transferring explicit or implicit feedback from the auxiliary domain to the target domain. Although the side information of review texts and item contents has been proven to be useful in recommendation, most existing works only use one kind of side information and cannot deeply fuse this side information with ratings. In this paper, we propose a Review and Content based Deep Fusion Model named RC-DFM for crossdomain recommendation. We first extend Stacked Denoising Autoencoders (SDAE) to effectively fuse review texts and item contents with the rating matrix in both auxiliary and target domains. Through this way, the learned latent factors of users and items in both domains preserve more semantic information for recommendation. Then we utilize a multi-layer perceptron to transfer user latent factors between the two domains to address the data sparsity and cold start issues. Experimental results on real datasets demonstrate the superior performance of RC-DFM compared with state-of-the-art recommendation methods.Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems
Leitura da trama polifônica: apreensão minuciosa do funcionamento da língua
Authors: Hanel Cerezoli Andréia Inês , Sasset Zanette Carla Roberta
Published: September 2019

A leitura enunciativa exige a participação ativa do leitor na reconstrução do sentido do discurso e, na perspectiva da teoria da polifonia, pode ser sistematizada em dois níveis: (1) descrição das diferentes vozes presentes no discurso; e (2) identificação das atitudes do locutor frente estas vozes. Este estudo mostra como os diferentes pontos de vista são importantes para que o leitor compreenda proficientemente o discurso, suas combinações e instruções. A fundamentação teórica está alicerçada em Ducrot, na Semântica Argumentativa (SA) ou Teoria da Argumentação na Língua (TAL), Flores e Teixeira (2005), Azevedo (2016), Negroni (2005), entre outros. Aplica-se, nesta investigação, a metodologia proposta por Ducrot, que consiste na observação, descrição e explicação do sentido do discurso para chegar à significação na língua. A descrição polifônica do discurso permite explicar, por exemplo, a atualização de um conector, mesmo ausente graficamente. Cientes da necessidade de um número maior de observações, descrições e explicações polifônicas, fica o convite aos leitores para mergulharem na trama de vozes do discurso.
Mathematics Trails: Shallow and Deep Gamification
Authors: Gurjanow Iwan , Oliveira Miguel , Zender Joerg , Santos Pedro A. , Ludwig Matthias
Published: September 2019

Mobile Math Trails for Europe (MoMaTrE) is an ongoing project to conceptualize and develop a fully gamified platform for creating, organizing and executing mathematics trails. We present some early experimental results of an empirical study from Frankfurt am Main with 218 pupils concerning the introduction of shallow gamification techniques in the platform. The study was based on the self-determination theory and therefore the Intrinsic Motivation Inventory was used. First results are mostly in line with other findings of the previous research literature, but also some interesting effects could be shown. The article concludes with a discussion of our plans for adding other (deeper) gamification elements.
ALF - a Framework for Evaluating Accelerated Learning in Industry
Authors: Petersen Sobah Abbas , Oliveira Manuel , Hestetun Kristin , Sørensen Anette Østbø
Published: September 2019

Games have long been considered as a means to support effective learning, motivate learners and accelerate their learning. Several successful studies using game-based learning are reported in the literature. However, there appears to be a research gap on systematically evaluating accelerated learning in game environments. The main research question we address in this paper is how can we evaluate accelerated learning in game-based learning environments? The main contribution of this paper will be a framework for evaluating accelerated learning in games (ALF). We will illustrate the use of this framework by describing studies conducted in the Norwegian industrial project ALTT (Accelerate Learning Through Technology), aimed at capacity building in the aluminium industry, where we have co-designed a game for accelerating learning about the electrolysis process for extracting aluminium and heat balance in the aluminium production cells.
¿Tesis o examen de grado? Un dilema para la formación investigativa
Authors: Romero-Ortega Armando , Sanz Cabrera Teresa
Published: September 2019

El nivel inicial del sistema educativo ecuatoriano urge de docentes con una sólida formación investigativa que les permita incidir de manera positiva en los nuevos contextos educativos. Un factor vinculado a este proceso es la elaboración del trabajo de titulación o el examen de grado que el estudiante debe rendir previo a su graduación. El objetivo de este estudio es determinar en qué medida se afecta la formación investigativa de los estudiantes de la Mención Parvularia de la Universidad Politécnica Salesiana con la aplicación de la nueva normativa que incluye el examen de grado como una alternativa al trabajo de titulación. Para ello se analiza el proceso de graduación de esta carrera en el período académico (2015-2016) a partir de algunos principios de la pedagogía crítica. Los datos revelan, por un lado, el incremento de la eficiencia terminal con la aplicación de los exámenes de grado; y, por otro, la disminución de experiencias de carácter investigativo propias de los trabajos de titulación, con lo cual se ve afectada la formación investigativa de los futuros docentes parvularios.
La conclusión a la que se llega es que esta formación debe ser re-significada por la carrera y dentro de ella, el trabajo de titulación necesita ser repotenciado para que cumpla sus objetivos educativos.
Material Clause Re-contextualization in Indonesian-English Translation
Authors: Susini Made , Sudipa I Nengah , Suparwa I Nyoman , Puspani Ida Ayu Made
Published: September 2019

This research is to investigate linguistic re-contextualization found in the translation from Indonesian into English. Applying pragmatic translation equivalence (House, 2015) and Systemic Functional Linguistics (Halliday and Matthiessen, 2014), it focuses on how material clause in Indonesian meditation text is rendered into English. The research finding shows that to achieve translation equivalence, Indonesian material clause is re-contextualized by rendering it into relational clause of attributive, relational clause of identification and material clause using different type of process. The re-contextualization results in shift of dematerialization and shift from one type of material clause into another type of material clause.
The Comparative Effect of Content-based Language Teaching and Task-based Language Teaching on Iranian Pre-intermediate EFL Learners’ Reading Skill
Authors: Namaziandost Ehsan , Nasri Mehdi , Ahmadi Sheida
Published: September 2019

This study aimed to explore the effectiveness of TBLT and CBLT on Iranian Pre-intermediate EFL learners’ reading skill. Forty pre-intermediate EFL learners were pretested and then homogenized and then divided to 2 classes, namely TBLT group and CBLT group. The CBLT group received reading instruction based on CBLT, while the RBLT group received reading instruction based on TBLT. After the treatment, both groups received a posttest. After analyzing the data through using paired and independent samples t-tests, it was shown that both groups (i.e., TBLT & CBLT) progressed considerably with regard to their reading skill from the pretest to the posttest, with the task-based language teaching group outperformed the content-based language teaching group in the posttest. It can be deduced that both task-based language teaching and content-based language teaching methods are effectual in reading comprehension instruction to Iranian Pre-intermediate EFL learners, with TBLT method being more effective.
Political News Discourse Analysis Based on an Attitudinal Perspective of the Appraisal Theory—Taking the New York Times’ Report China-DPRK Relations as an Example
Authors: Jin Jinxiu
Published: September 2019

The relationship among China, the United States and North Korea has already been a focus of international politics. From June 19 to 20, North Korea leader Kim Jong-un ended his third visit to China within 100 days. This is also his three consecutive visits to China since he took office in December 2011. The high density and frequency are not only rare in the history of China-DPRK relations, but also seem to be unique in the history of international relations, indicating that China-DPRK relations are welcoming new era. This paper selects the New York Times’ report on China-DPRK relations as an example, which is based on an attitudinal perspective of the appraisal theory to analyze American attitudes toward China. Attitudes are positive and negative, explicit and implicit. Whether the attitude is good or not depends on the linguistic meaning of expressing attitude. The meaning of language is positive, and the attitude of expression is positive; the meaning of language is negative, and the attitude of expression is negative. The study found that most of the attitude resources are affect (which are always negative affect), which are mainly realized through such means as lexical, syntactical and rhetorical strategies implicitly or explicitly. All these negative evaluations not only help construct a discourse mode for building the bad image of China but also are not good to China-DPRK relations. The United States wants to tarnish image of China and destroy the relationship between China and North Korea by its political news discourse.
Testing the Depths to 1.0 and 2.5  km/s Velocity Isosurfaces in a Velocity Model for Japan and Implications for Ground‐Motion Modeling
Authors: Zhu Chuanbin , Cotton Fabrice , Pilz Marco
Published: September 2019

In the Next Generation Attenuation West2 (NGA‐West2) project, a 3D subsurface structure model (Japan Seismic Hazard Information Station [J‐SHIS]) was queried to establish depths to 1.0 and 2.5  km/s velocity isosurfaces for sites without depth measurement in Japan. In this article, we evaluate the depth parameters in the J‐SHIS velocity model by comparing them with their corresponding site‐specific depth measurements derived from selected KiK‐net velocity profiles. The comparison indicates that the J‐SHIS model underestimates site depths at shallow sites and overestimates depths at deep sites. Similar issues were also identified in the southern California basin model. Our results also show that these underestimations and overestimations have a potentially significant impact on ground‐motion prediction using NGA‐West2 ground‐motion models (GMMs). Site resonant period may be considered as an alternative to depth parameter in the site term of a GMM.
Effectiveness of mirror therapy in the motor recovery of upper extremity in the post stroke hemiplegic patients: a randomized controlled trial in a tertiary care hospital in Manipur, Northeast India
Authors: Yumnam Ningthemba , Akoijam Joy Singh , Singh Longjam Nilachandra , Oinam Joymati
Published: September 2019

Background: Stroke is a leading cause of disability and majority of the stroke survivors experience upper extremity functional limitations. Therefore, the objective of this study was to evaluate the effectiveness of mirror therapy in addition with a conventional upper limb rehabilitation program in a post stroke hemiplegic patient.Methods: A randomized controlled trial was conducted among 72 post stroke patients aged 35-65 years having hemipa¬resis attending the PMR department, RIMS, Manipur from 2013 to 2016. Assessment was done at baseline, 1 and 6 months for FIM self care, Brunnstrom stages of motor recovery and MAS for spasticity. Both the group participated in a stroke rehabilitation program and study group was given mirror therapy in addition. Descriptive statistics such as mean±SD and inferential statistics like Chi-square test, Student’s t test, and ANOVA were used. A p-value < 0.05 was taken as statistically significant.Results: There was an improvement in Brunnstrom stage and FIM self-care score in both groups, but the post-treatment score was significantly higher in the mirror therapy group. Statistically significant difference in FIM self care and motor recovery between the study and control groups was noted [1.1±0.38 (study) versus 0.88±0.32 (control) for motor recovery and 34.1±2.59 (study) versus 29.5±4.58 (control) for FIM self care]. However, no significant difference was seen in spasticity between the groups.Conclusions: Mirror therapy used in addition to conventional stroke rehabilitation program was found to be effective in the recovery of upper extremity motor functions and daily self care activities in post stroke hemiplegic patients.
Impact of gas flaring in Ubeji metropolis of Delta State Nigeria: a comparative survey of environment health effects
Authors: Akuirene Omatseye Alero , Adjene Josiah O. , Obi Nkemdilim I. , Nwose Ezekiel Uba
Published: September 2019

Background: The impact of gas flaring in Ubeji metropolis, relative to other communities of Delta is of interest. This study assessed the environmental impact of gas flaring in Ubeji metropolis of Delta State. The objective of this study is to study the comparative assessment of the environmental impacts of gas flaring on five communities of unequal distance from gas flaring site.

Methods: Questionnaire survey was used to evaluate three research questions that included the level of knowledge in the survey communities regarding health impact of gas flaring; prevalence level of assessed health conditions in Ubeji metropolis relative to communities farther from or nearer to Warri gas flaring site; and perceived impact of gas flaring on air quality indicated by health and environmental toxicity indices.  

Results: Descriptive evaluation of data shows Ubeji appears to have the highest proportion of persons suffering respiratory problems, but the figure for family members is less than some other communities. The respondents nearer flaring site show more awareness compared to those farther away. Critical evaluation showed no directional change in prevalence of disease linked to gas flaring. There is linear relationship between ‘distance to gas flare site’ and ‘toxic impact on air quality’.

Conclusions: This report submits further data to the discourse that, on the basis of nearness to gas flaring site, there is significant difference between communities experience of environmental and health impact.

 
A Política de Saúde Mental na atualidade e o avanço do conservadorismo
Authors: Costa Tereza Cristina Ribeiro da
Published: September 2019

Este texto foi construído a partir dos estudos produzidos na tese de doutorado defendida na Universidade Federal de Pernambuco, atualizando com dados mais recentes dos novos contornos que tem tomado a política de saúde mental na atualidade. Na atual crise, faz-se necessário pensar no avanço do conservadorismo na Política de Saúde Mental e seus dilemas contemporâneos frente a ofensiva neoliberal. No Brasil, a atual Política de Saúde mental tem sido fortemente impactada pela onda conservadora que tem tomada as discussões políticas, econômicas e sociais na atualidade.
Third Parties’ Legal Protection over Agreed Authorized Capital Amount by Founders in Limited Liability Companies
Authors: Nugraha Xavier , Murti Krisna , Putri Saraswati
Published: September 2019

On July 14, 2016, the Government enacted the Government Regulation Number 29 of 2016 regarding Amendments in Authorized Capital of a Limited Liability Company (LLC). Article 1 paragraph (3) of the regulation showed that the amount of authorized capital was submitted to the agreement of the LLC founders. This regulation was issued in order to increase Indonesia’s ‘ease of doing business’ rank, especially in ‘starting a business.’ This article aims to examine the legal protection for the third party over the amount of authorized capital based on the agreement of the LLC founders using the study of dogmatic law. Regulations referenced are Law Number 40 of 2007 regarding Limited Liability Companies and Government Regulation Number 29 of 2016 regarding Amendments in Authorized Capital of LLC. Based on the results of this study, it was found that the determination of authorized capital based on the agreement of LLC founders has neglected the protection of the third parties. This manifested particularly in protecting minority investors and resolving insolvency. Through the enactment of authorized capital based on the agreement of the founders, the mechanism of preventive and repressive legal protection to the third parties are assumed to be eliminated.

Keywords: The Authorized Capital, Limited Liability Company, Agreement, Legal Protection.
ACCESS TO EU PUBLIC FINANCIAL SUPPORT AND COMMERCIALIZATION OF INNOVATIONS: EVIDENCE FROM HOSPITALITY INDUSTRIES IN SPAIN AND CROATIA
Authors: Stojčić Nebojša
Published: September 2019

Purpose – The purpose of this paper is to assess the impact of public financial support to
innovations provided through EU funds on success of innovation process in hospitality industries
of two leading tourism countries in Europe, Spain and Croatia during 2012-2014 period. The paper
investigates whether innovation push channels improve ability of firms to increase share of
turnover generated from new products and services. We distinguish between commercialization of
innovations which are new to the market and those that are new to the firm but have already been
known to other competitors.
Methodology – The study relies on data from the most recent round of Community Innovation
Survey, a confidential dataset compiled by Eurostat on innovation activities of firms in EU member
states. Treatment analysis is applied to assess whether EU funded public support creates
additionality effects or it leads to quiet life behaviour. The model used also introduces number of
firm specific characteristics such as previous innovation experience, pattern of innovation
development, licensing behaviour, firm size, ownership and the quality of organizational human
capital.
Findings – Results of investigation point to the positive impact of EU funds in Spain when it comes
to share of sales coming from radical innovations and positive in both countries when it comes to
share of sales from innovations which are new to the firm but have been known to the market as
well as unchanged or marginally modified products. This signals that in both countries firms
exploit EU funds to apply solutions proven successful by others but do not engage in disruptive
innovations required for stronger market differentiation and generation of above average returns.
The ability to access EU funds for innovations is positively related to firm size and the quality of
organizational human capital. Moreover, firms that are part of foreign owned groups have higher
chances of accessing EU funds for innovations. Together with previous findings this signals that
experience, knowledge transfer and absorptive capacity of firms are the key factors behind the
ability of firms to benefit from public support to innovation.
Contribution – In development of innovations firms are often constrained with lack of knowledge,
skills and financial resources. Public support is essential in such circumstances for firms that are
interested in pursuit of innovations. There is longstanding discussion, however, whether public
funds are efficient in facilitating innovation and this debate has particularly been pronounced at
EU level. Our study thus provides direct inputs for formulation of future innovation policies that
can help to improve performance of firms in hospitality industries.
PILIHAN KODE BAGI SISWA DI PESANTREN ULIL ALBAB (KAJIAN SOSIOLINGUISTIK)
Authors: Wahidah Baiq Yulia Kurnia , Djatmika D. , Marmanto Sri
Published: September 2019

This research aims to find and describe the selection of code in to three domains as well as the types of code choice in accordance with the context in the environment of Ulil Albab boarding schools. The data provision in this study is linguistic data from visual or audio visual recording by using taping and gathering method. The data is primary and secondary. Primary data is the result of visual or audio visual recording of the communicating interaction event and the secondary data is the result of previous research review.

In analysis, the data are utterances that contain three kind of code switching i.e metaphorical, conversational, and situational. Thereby, the three data of speech in kind of code switching are the sign for the presence of code selection. The object of this study is interacting event that contains the form of code choice.

As the results of this research are the most usage selection of code founded in the realm of friendship, the types selection of code most used in all domains was conversational. From these three domains, each of them was using language variation and contexs the dominance usage of language was Sasak. So, it can be concluded that in interaction process, the usage of code switching in each domain was appropriated with language variation by speaker, in order that the interaction gone well.
KAJIAN TERJEMAHAN ARTIKEL FLEEING TERROR, FINDING REFUGE DAN TERJEMAHANNYA MENCARI TEMPAT BERLINDUNG DALAM HAL STRUKTUR DAN POLA PENGEMBANGAN TEMA (PENDEKATAN LINGUISTIK SISTEMIK FUNGSIONAL)
Authors: Aji Satriya Bayu , Nababan Mangatur , Wiratno Tri
Published: September 2019

This research aims to describe: (1) the March 2015 National Geographic article Fleeing Terror, Finding Refuge and its March 2015 National Geographic Indonesia Indonesian translation Mencari Tempat Berlindung’s thematic structure and progression, (2) the translation techniques employed and its effect on the translation’s thematic structure and progression, and (3) its effect on the translation quality. This study belongs to the qualitative research at the descriptive level, employs embedded case study approach, and focuses on the translation product. The data consist of the Themes of the National Geographic Maret 2015 article Fleeing Terror, Finding Refuge and its National Geographic Indonesia Maret 2015 Indonesian translation Mencari Tempat Berlindung collected through content analysis and the result of the informant’s translation quality assessment collected through questionnaire and focus group discussion. This study revealed that the translation employs more marked Topical Theme than the source text and the percentage of the translation’s Rheme-based progressions (the simple linear and split Rheme progression) is higher than the source text’s. The thematic structure shifts—mostly a shift from the unmarked to the marked Topical Theme and vice-versa or a change in the Topical Theme constituent—can be caused by the use of these six translation techniques: transposition, modulation, reduction, amplification, established equivalent, and particularization. Of these six, particularization does not cause any Thematic progression shift. These six techniques that can cause a shift in the Thematic structure can also decrease the translation’s quality.

 

Keywords: Thematic structure, Thematic progression, translation techniques, translation quality assessment
STAD STRATEGIES OF DEFENDING SELF IMAGE IN THE PUBLIC SPEAKING DELIVERED BY MR TOURISM
Authors: Afrinda Putri Dian , Yanda Diyan Permata
Published: September 2019

This article aims to explain how public speaking strategies for Mr. Tourism Sumatera Barat 2018 as a form of defending self image. The strategy that can be used is the strategy of telling Brown and Levinson about positive and negative faces. As the center of attention, a speaker must be able to maintain the face for an audience (audience) with the material presented. Public speaking training can be given in the form of material presentation and training using STAD type cooperative learning model. By using STAD type model, it is expected that the training participants can learn in their respective groups with directed instructors for the preparation of individual presentation. The results of the activities show that STAD model is effectively used to practice speech or public speaking. This is evident from the activity of the trainees following the material and confidence in public speaking.
ABREVIASI DALAM PERCAKAPAN SEHARI-HARI DI MEDIA SOSIAL: SUATU KAJIAN MORFOLOGI (ABBREVIATION IN EVERYDAY CONVERSATION ON SOCIAL MEDIA: A MORPHOLOGICAL STUDY)
Authors: Cenderamata Rengganis Citra , Sofyan Agus Nero
Published: September 2019

This research is entitled “Abbreviation in Everyday Converstion on Social Media: A Morphological Study”. The method used in this study is a qualitative method. The data are taken from everyday conversation on social media such as line, whatsapp, facebook, twitter, and instagram. The theory are used abbreviation process and slang language. The aims of this study are to describe the common features of abbreviation process used by young and adult Indonesian people. Based on the research, it is indicated that the participants used three most common abbreviation processes are acronym, blending, and clipping in everyday conversation on social media. Acronym and blending found as the most common abbreviation process among the three features. As for the reasons of this language phenomenon occurrence identified are the participants intend to save time, fill the communication gap or barrier among the users, and indicate the social group.
Cost-effectiveness of two long-lasting insecticidal nets delivery models in mass campaign in rural Mozambique
Authors: Arroz Jorge , Candrinho Baltazar , Mendis Chandana , Lopez Melanie , Martins Maria do Rosário Oliveira
Published: September 2019

Objective The aim is to compare the cost-effectiveness of two long-lasting insecticidal nets (LLINs) delivery models (standard vs new) in universal coverage (UC) campaigns in rural Mozambique. Results The total financial cost of delivering LLINs was US$ 231,237.30 and US$ 174,790.14 in the intervention (302,648 LLINs were delivered) and control districts (219,613 LLINs were delivered), respectively. The average cost-effectiveness ratio (ACER) per LLIN delivered and ACER per household (HH) achieving UC was lower in the intervention districts. The incremental cost-effectiveness ratio (ICER) per LLIN and ICER per HH reaching UC were US$ 0.68 and US$ 2.24, respectively. Both incremental net benefit (for delivered LLIN and for HHs reaching UC) were positive (intervention deemed cost-effective). Overall, the newer delivery model was the more cost-effective intervention. However, the long-term sustainability of either delivery models is far from guaranteed in Mozambique’s current economic context.
AN ALTERNATIVE MATRIX TRANSFORMATION TO THE F TEST STATISTIC FOR CLUSTERED DATA
Authors: Intarapak Sukanya , Supapakorn Thidaporn
Published: September 2019

For the regression analysis of clustered data, the error of cluster data violates the independence assumption. Consequently, the test statistic based on the ordinary least square method leads to incorrect inferences. To overcome this issue, the transformation is required to apply to the observations. In this paper we propose an alternative matrix transformation that adjusts the intra-cluster correlation with Householder matrix and apply it to the F test statistic based on generalized least squares procedures for the regression coefficients hypothesis. By Monte Carlo simulations of the balanced and unbalanced data, it is found that the F test statistic based on generalized least squares procedures with Adjusted Householder transformation performs well in terms of the type I error rate and power of the test.
GENERALIZED BAYES ESTIMATION OF SPATIAL AUTOREGRESSIVE MODELS
Authors: Chaturvedi Anoop , Mishra Sandeep
Published: September 2019

The spatial autoregressive (SAR) models are widely used in spatial econometrics for analyzing spatial data involving spatial autocorrelation structure. The present paper derives a Generalized Bayes estimator for estimating the parameters of a SAR model. The admissibility and minimaxity properties of the estimator have been discussed. For investigating the finite sample behaviour of the estimator, the results of a simulation study have been presented. The results of the paper are applied to demographic data on total fertility rate for selected Indian states.
APPLICATION OF THE STRATEGY COMBINING MONETARY UNIT SAMPLING AND THE HORVITZ– THOMPSON ESTIMATOR OF ERROR AMOUNT IN AUDITING – RESULTS OF A SIMULATION STUDY
Authors: Janusz Bartłomiej
Published: September 2019

Auditors need information on the performance of different statistical methods when applied to audit populations. The aim of the study was to examine the reliability and efficiency of a strategy combining systematic Monetary Unit Sampling and confidence intervals for the total error based on the Horvitz-Thompson estimator with normality assumption. This strategy is a possible alternative for testing audit populations with high error rates. Using real and simulated data sets, for the majority of populations, the interval coverage rate was lower than the assumed confidence level. In most cases confidence intervals were too wide to be of practical use to auditors. Confidence intervals tended to become wider as the observed error rate increased. Tests disclosed the distribution of the Horvitz-Thompson estimator was not normal. A detailed analysis of the distributions of the error amount in the examined real audit populations is also given.