Miracle-dz/UniFAR-FTUs huggingface dataset:
THE CAPACITY OF ENGLISH SUBJECT TEACHERS’ WORKING GROUP (MGMP) IN SUPPORTING TEACHER PROFESSIONAL DEVELOPMENT (A Case Study of English MGMP of Senior High Schools in Kota Pontianak)

Rodhi, S891208043. 2014. The Capacity of English Subject Teachers’ Working Group (MGMP) in Supporting Teacher Professional Development (A Case Study of English MGMP of Senior High Schools in Kota Pontianak). First Consultant: Dra. Dewi Rochsantiningsih, M.Ed., Ph.D.; Second Consultant: Dr. Sumardi, M.Hum. Thesis: English Education Department, Graduate School, Faculty of Teacher Training and Education, Sebelas Maret University Surakarta. This case study was designed to describe a) the organization structure of English MGMP of senior high schools; and b) how English MGMP of senior high schools utilized its capacity and functions in promoting teacher professional development (TPD) of the members. The study involved six teachers selected randomly as participants from the accessible schools. In order to have MGMP descriptively and well pictured, the study utilized interview, documentation, and observation for collecting data, producing a collection of interview transcripts, documents, fieldnotes, and research journal. Meanwhile, to guarantee the validity of those data, triangulation and member checking techniques were utilized. Three-step data anlysis model, a) familiarizing and organizing, b) coding and reducing, and c) interpreting and representing, were adopted to present reasonable conclusions. In one side the findings summarize that in general all the repondents asserted the benefits of MGMP forums for classroom practices. In the other side, nevertheless, the findings conversely conclude that MGMP capacity and functions were not optimally made use for promoting TPD as a result of being unwell managed in terms of personnel, program schedules, and program coverage and also being insufficiently supported by local authority of education in terms of regular mentoring towards MGMP activities and management. Key words: Teacher professional development, subject teachers’ working group (MGMP), MGMP capacity
Spatio-Temporal Fusion Based Convolutional Sequence Learning for Lip Reading

Current state-of-the-art approaches for lip reading are based on sequence-to-sequence architectures that are designed for natural machine translation and audio speech recognition. Hence, these methods do not fully exploit the characteristics of the lip dynamics, causing two main drawbacks. First, the short-range temporal dependencies, which are critical to the mapping from lip images to visemes, receives no extra attention. Second, local spatial information is discarded in the existing sequence models due to the use of global average pooling (GAP). To well solve these drawbacks, we propose a Temporal Focal block to sufficiently describe short-range dependencies and a Spatio-Temporal Fusion Module (STFM) to maintain the local spatial information and to reduce the feature dimensions as well. From the experiment results, it is demonstrated that our method achieves comparable performance with the state-of-the-art approach using much less training data and much lighter Convolutional Feature Extractor. The training time is reduced by 12 days due to the convolutional structure and the local self-attention mechanism.
WS 3(3) 001-008

Wastewater reclamation and reuse is being viewed increasingly as a sustainable approach to integrated water resources management in many countries including Australia. The technical feasibility of reclamation and reuse has been demonstrated by a number of successful projects. The current state-of-the art of reclamation technologies can produce water of any desired quality (including potable quality). However, the increasing number of efficient treatment processes has made the selection of an optimum treatment train a difficult task for planners and decision-makers. A decision support system (DSS) can be particularly useful in wastewater reclamation and reuse as it can provide assistance in the evaluation and selection of treatment alternatives for a given reuse application before exhaustive simulation or pilot studies are conducted. This paper highlights the ongoing research on the development of a computer based DSS named MOSTWATAR© (which stands for Model for Optimum Selection of Technologies for WAstewater Treatment And Reuse). MOSTWATAR© has a database of the performance characteristics and costs of commonly used reclamation technologies and an optimization module based on genetic algorithms to generate and optimize treatment trains. It also contains detailed reuse guidelines applicable in the various Australian States. This model is intended to assist planners and decision-makers in the techno-economic assessment of reclamation technologies and aid in the selection of the best 5 treatment trains for a given end use and location, wastewater characteristics, and flow rate. This paper describes salient features of the MOSTWATAR© package and demonstrates its application to a case study. The results from user-generated options are presented and it is shown that this model can be a very useful tool for selecting the best treatment trains for wastewater reclamation and reuse.
Efficient Verifiable Secret Sharing with Share Recovery in BFT Protocols

Byzantine fault tolerant state machine replication (SMR) provides powerful integrity guarantees, but fails to provide any privacy guarantee whatsoever. A natural way to add such privacy guarantees is to secret-share state instead of fully replicating it. Such a com- bination would enable simple solutions to difficult problems, such as a fair exchange or a distributed certification authority. However, incorporating secret shared state into traditional Byzantine fault tolerant (BFT) SMR protocols presents unique challenges. BFT protocols often use a network model that has some degree of asynchrony, making verifiable secret sharing (VSS) unsuitable. However, full asynchronous VSS (AVSS) is unnecessary as well since the BFT algorithm provides a broadcast channel. We first present the VSS with share recovery problem, which is the subproblem of AVSS required to incorporate secret shared state into a BFT engine. Then, we provide the first VSS with share recovery solution, KZG-VSSR, in which a failure-free sharing incurs only a constant number of cryptographic operations per replica. Finally, we show how to efficiently integrate any instantiation of VSSR into a BFT replication protocol while incurring only constant overhead. Instantiating VSSR with prior AVSS protocols would require a quadratic communication cost for a single shared value and incur a linear overhead when incorporated into BFT replication. We demonstrate our end-to-end solution via a a private key-value store built using BFT replication and two instantiations of VSSR, KZG-VSSR and Ped-VSSR, and present its evaluation.
Sources of growth in income and inequality among ethnic groups in Malaysia for 1970-2000

This paper examines sources of income growth over the period 1970-2000, with emphasis given on income distribution among major ethnic groups in Malaysia. The framework within the analyses are carried out is a structural decomposition analysis (SDA), applied to a socio-economic system which represented by a social accounting matrix (SAM). It offers a wide-decomposition analysis, separates out the income changes into the effects that determined primarily by production structure and secondarily through re-distribution mechanism. Results consistently indicate that expansion in exports and technological change in using labor and capital are the main determinants for the income changes for all individual ethnic groups. In relative measures however, these two determinants (as well as other determinants) have limited effects on the Malay compared to the Chinese and Indian. The similar outcomes hold for the effects on employment changes. The reason for differences is that a large number of Malay workers are employed in the public services sectors, the sectors which have limited structural changes and low productivity. In one hand, inequality has been reduced through extensive government supports but on the other hand, it is not a sustainable income growth for the Malay.
Processing of Yam Paste (Amala): A Product of Fermented Yam (Dioscorea rotundata) Flour

This study was designed to evaluate the processing of yam paste (amala): A product of fermented yam (Discorea rotundata) flour. The sliced yam samples were fermented for 72 hours. The microorganisms were isolated and identified. The pH, TTA, temperature, proximate, anti-nutrient, mineral composition and sensory analyses of the sample were determined. The microbial loads increased with fermentation time. The isolates identified were Pseudomonas aureginosa, Staphylococcus aureus, Bacillus subtilis, Lactobacillus plantarum, Lactococcus lactis, Aspergillus niger, Saccharomyces cerevisiae, Fusarium oxysporium, and Candida tropicalis. pH decreased from 6.4 to 4.9 while TTA and temperature increased from 0.16 to 4.24% and 28oC to 34oC. The crude protein (2.46%) and moisture contents (12.32%) increased in the fermented sample with a decrease in ash (0.70%), fat (0.19%) and carbohydrate contents (80.76%). The saponin and phytate decreased from 31.29 to 11.45mg/g and 11.12 to 5.77mg/g. The fermented sample showed an overall acceptability. Improvement in the nutritional and reduction in antinutritional contents of the fermented sample suggests its usefulness as a food source for humans. The microorganisms associated with fermented yam tubers, processing of yam tubers into flour to produce amala and determining the nutritional and antinutrient content of both unfermented and fermented yam. The fermentation process improving the quality and nutrient content of the yam tubers suggested the method as more promising in producing food for humans.
Role of Context in the (Re)construction of Political Ideologies and Identities

Political discourse is considered an important instrument to (re)construct political identities and ideologies, by influencing individual’s perceptions in a socio-political scenario. The present study titled “Role of Context in the Re(construction) of Political Ideologies and Identities” aims to explore how context is used discursively in the political discourse presented in the manifestos of seven selected Pakistani political parties(2013) to (re)construct people’s political identities and ideologies. The manifestos of seven political parties of Pakistan are selected to be critically analyzed through Qualitative mode of inquiry, on the basis of popularity and provincial representation. The political parties included are Pakistan Muslim League (N), Pakistan Tahreek-e-Insaf, Pakistan People’s Party, Pakistan Muslim League (Q) and Muttahida Quami Movement, Baloch National Party (BNP) and Awami National Party (ANP). Social Identity Theory by Tajfel and Turner (1979) is used as the theoretical framework and Critical Discourse Analysis is used as a method for the present study. It was concluded that all political parties under study brought context into use as a discursive strategy to portray positive self-image and negative image of other parties. This was done by the political parties in order to transform people’s political ideologies and identities in their favor.
Global-as-View Ontology-Based Data Access for Relational Data

Ontology Based Data Access (OBDA) is a paradigm with which different data sources can be mapped onto a global schema that can be queried. A use case of OBDA is to integrate relational data into knowledge graphs. In this thesis a formal framework for OBDA systems is presented. Based on this framework the OBDA system Ultrawrap is formalized. Ultrawrap has been reimplemented, extended and the space consumption of the system has been optimized. Results of the Texas Benchmark show that the reimplemented system is averagely 3.16 times faster than Ultrawrap and averagely 1.87 times faster than the state of the art OBDA system Ontop. Furthermore, the execution times of the reimplemented system and the optimized reimplementation are comparable, while the space consumption of the optimized system is reduced by 55% compared to the unoptimized version.
Inexact Global FOM and GMRES Methods for Solving Linear Systems with Multiple Right-Hand Sides

Inexact Krylov subspace methods are very effective in some scientific applications where an inexact matrix-vector product appears naturally. Several authors have studied different aspects of the use of inexact matrix-vector product in iterative methods. In this chapter, using the Kronecker product-based technique, we generalize the existing results to global methods for matrix equations. The corresponding methods are called inexact global Krylov subspace methods. Theory of these methods is established, especially the breakdown and convergence of the inexact global generalized minimum residual method. Some numerical experiments are reported to illustrate our results.
Zero-Shot Emotion Recognition via Affective Structural Embedding

Image emotion recognition attracts much attention in recent years due to its wide applications. It aims to classify the emotional response of humans, where candidate emotion categories are generally defined by specific psychological theories, such as Ekman’s six basic emotions. However, with the development of psychological theories, emotion categories become increasingly diverse, fine-grained, and difficult to collect samples. In this paper, we investigate zero-shot learning (ZSL) problem in the emotion recognition task, which tries to recognize the new unseen emotions. Specifically, we propose a novel affective-structural embedding framework, utilizing mid-level semantic representation, i.e., adjective-noun pairs (ANP) features, to construct an affective embedding space. By doing this, the learned intermediate space can narrow the semantic gap between low-level visual and high-level semantic features. In addition, we introduce an affective adversarial constraint to retain the discriminative capacity of visual features and the affective structural information of semantic features during training process. Our method is evaluated on five widely used affective datasets and the perimental results show the proposed algorithm outperforms the state-of-the-art approaches.
Scientific Visualization in Oil and Gas Exploration

In this paper we present a method for interpretation of statistical results from discriminant analysis. The specific attention has been given to the application of Scientific Visualization to the presentation of results from statistical analysis. Techniques are presented to aUow a geologist in oil and gas exploration, to make a visual analysis of the statistical results. In particular, we explain how to convert multidimensional geological data into a single parameter, which is later presented in 2D and 3D space. A discriminant criterion is developed and then used to classifY each of the multivariate observations into one of two groups: producers and nonproducers. The posterior probability of membership in each group for the test data is presented as the production probability. Furthermore, this probability governs the colour and density during the rendering process.
UDC 544

Possible mechanisms of interaction of dimethyl carbonate with polydimethylsiloxane have been examined by density functional theory method with exchange-correlation functional B3LYP and basis set 6-31G(d,p). A carbon atom attack of the carbonyl group of dimethyl carbonate molecule at the oxygen atom of the siloxane bridge of polydimethylsiloxane with simultanious ester oxygen atom attack of dimethyl carbonate at the silicon atom of organosilicon polymer have been found to be the most probable.
The Critical Role of Data Mining for Analyzing Real-World Event Sequences

There are many existing techniques for visualizing temporal data, but the complexity of real-world temporal data may undermine certain approaches. Many existing techniques follow consistent and helpful conventions of mapping time to the horizontal axis, aligning multiple timelines along the vertical axis, and assign various event types to categorical visual encodings. A variety of techniques, such as LifeLines [7] and EventFlow [3], have shown great promise in aiding the analysis of temporal patterns. However, as the volume of temporal data increases, along with the data’s inherent noise, we argue that visualization and interaction techniques alone may not empower analysts sufficiently. In particular, the diversity of event types, the presence of both relevant and irrelevant event types mixed together, and the lack of strict orderliness in real world data pose many challenges. To this effect, we argue that integrating data mining techniques is critical for making sense out of real-world temporal data for many domains. While automated data mining techniques can’t be a substitute for user-centered exploration, data mining techniques can empower users in critical ways. Visualizing entire event sequences because impossible as the volume of data grows. Simplifying raw event sequences using filtering and aggregation is promising [3], but this may create a reliance of domain expertise. Often, it may be hard to see patterns due to unimportant events distributed through the sequences. In addition, temporal visualization techniques often do not scale to thousands of different event types. However, if we can utilize data mining techniques that effectively unearth statistically significant patterns, simpler and more concise visualizations can be used for communication by focusing on these patterns.
Findings of the Third Workshop on Neural Generation and Translation

This document describes the findings of the Third Workshop on Neural Generation and Translation, held in concert with the annual conference of the Empirical Methods in Natural Language Processing (EMNLP 2019). First, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the two shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language.
Urinary Biomarkers of Inflammation and Oxidative Stress Are Elevated in Obese Children and Correlate with a Marker of Endothelial Dysfunction

Obesity is a state of chronic low-level inflammation closely associated with oxidative stress. Childhood obesity is associated with endothelial dysfunction, inflammation, and oxidative stress markers individually. This study was aimed at determining the association between the biomarkers of inflammation, oxidative stress, and endothelial dysfunction in urine samples of healthy, overweight, and obese children. Eighty-eight elementary school children aged between 6 and 10 years participated in this study. Anthropometric measurements were measured using WHO recommendations. The biomarkers of low-grade inflammation such as C-reactive protein (CRP), interleukin-6 (IL-6), and α-1-acid glycoprotein (AGP); oxidative stress markers such as 8-isoprostane and 8-hydroxy-2′-deoxyguanosine (8-OHdG); and endothelin-1 (ET-1) were analyzed in urine samples. The area under the curve (AUC) by the receiver operating characteristics (ROC) was analyzed to identify the best urinary biomarker in childhood obesity. Linear regression and Pearson correlation were analyzed to determine the association between the parameters. The obese participants have significantly increased levels of CRP, AGP, IL-6, and 8-isoprostane compared to normal-weight participants. The overweight participants had significantly increased levels of ET-1 and 8-OHdG but not the obese group compared to the NW group. The AUC for urinary CRP (AUC: 0.847, 95% CI: 0.765-0.930; p < 0.0001) and 8-isoprostane (AUC: 0.857, 95% CI: 0.783-0.932; p < 0.0001) showed a greater area under ROC curves compared to other inflammatory and oxidative markers. The urinary CRP and 8-isoprostane significantly correlated with the obesity measures (body mass index, waist circumference, and waist-to- height ratio) and ET-1, inflammatory, and oxidative markers. The increased urinary inflammatory markers and 8-isoprostane can serve as a noninvasive benchmark for early detection of the risk of developing cardiovascular disease.
Unsupervised Joint 3D Object Model Learning and 6D Pose Estimation for Depth-Based Instance Segmentation

In this work, we propose a novel unsupervised approach to jointly learn the 3D object model and estimate the 6D poses of multiple instances of a same object, with applications to depth-based instance segmentation. The inputs are depth images, and the learned object model is represented by a 3D point cloud. Traditional 6D pose estimation approaches are not sufficient to address this problem, where neither a CAD model of the object nor the ground-truth 6D poses of its instances are available during training. To solve this problem, we propose to jointly optimize the model learning and pose estimation in an end-to-end deep learning framework. Specifically, our network produces a 3D object model and a list of rigid transformations on this model to generate instances, which when rendered must match the observed point cloud to minimizing the Chamfer distance. To render the set of instance point clouds with occlusions, the network automatically removes the occluded points in a given camera view. Extensive experiments evaluate our technique on several object models and varying number of instances in 3D point clouds. We demonstrate the application of our method to instance segmentation of depth images of small bins of industrial parts. Compared with popular baselines for instance segmentation, our model not only demonstrates competitive performance, but also learns a 3D object model that is represented as a 3D point cloud.
Bayesian Gaussian models for point referenced spatial and spatio-temporal data

When data is correlated both spatially and temporally, spatial and spatio-temporal modelling is useful for meaningful interpretation of the parameters of the covariates and for reliable predictions. In this paper we discuss some modelling strategies for point referenced spatial and spatio-temporal data. We describe Gaussian models in this context and use Bayesian hierarchical approaches for model based inference and predictions through the Markov chain Monte Carlo (MCMC) algorithm. Yearly average precipitation data from Western Australia is used to illustrate the models.
Group-Wise Deep Object Co-Segmentation With Co-Attention Recurrent Neural Network

Effective feature representations which should not only express the images individual properties, but also reﬂect the interaction among group images are essentially crucial for real-world co-segmentation. This paper proposes a novel end-to-end deep learning approach for group-wise object co-segmentation with a recurrent network architecture. Speciﬁcally, the semantic features extracted from a pre-trained CNN of each image are ﬁrst processed by single image representation branch to learn the unique properties. Meanwhile, a specially designed Co-Attention Recurrent Unit (CARU) recurrently explores all images to generate the ﬁnal group representation by using the co-attention between images, and simultaneously suppresses noisy information. The group feature which contains synergetic information is broadcasted to each individual image and fused with multi-scale ﬁne-resolution features to facilitate the inferring of co-segmentation. Moreover, we propose a groupwise training objective to utilize the co-object similarity and ﬁgure-ground distinctness as the additional supervision. The whole modules are collaboratively optimized in an end-to-end manner, further improving the robustness of the approach. Comprehensive experiments on three benchmarks can demonstrate the superiority of our approach in comparison with the state-of-the-art methods.