Reviewerly/exHarmony huggingface dataset:
Incorporating Syntactic Knowledge in Neural Quality Estimation for Machine Translation

Translation quality estimation aims at evaluating the machine translation output without references. State-of-the-art quality estimation methods based on neural networks have certain capability of implicitly learning the syntactic information from sentence-aligned parallel corpus. However, they still fail to capture the deep structural syntactic details of the sentences. This paper proposes a method that explicitly incorporates source syntax in neural quality estimation. Specifically, the parse trees of source sentences are linearized, and the sequence labels are combined with the source sequence through hierarchical encoding to obtain a more complete and deeper source encoding vector. The hidden relationships between the source syntactic structure and the translation quality are modeled to discover the syntactic errors in the translation. Experimental results on WMT17 quality estimation datasets show that the sentence-level Pearson correlation score and the word-level F1–mult score can both be improved by the syntactic knowledge.
A Fast and Accurate Partially Deterministic Morphological Analysis

This paper proposes a partially deterministic morphological analysis method for improved processing speed.Maximum matching is a fast deterministic method for morphological analysis.However, the method tends to decrease performance due to lack of consideration of contextual information.In order to use maximum matching safely, we propose the use of Context Independent Strings (CISs), which are strings that do not have ambiguity in terms of morphological analysis.Our method first identifies CISs in a sentence using maximum matching without contextual information, then analyzes the unprocessed part of the sentence using a bi-gram-based morphological analysis model.We evaluate the method on a Japanese morphological analysis task.The experimental results show a 30% reduction of running time while maintaining improved accuracy.
An End-to-End Practical System for Road Marking Detection

Road marking is a special kind of symbol on the road surface, used to regulate the behavior of traffic participants. According to our survey, it seems that no papers has yet proposed a mature, highly practical method to detect and classify these important fine-grained markings. Deep learning techniques, especially deep neural networks, have proven to be effective in coping with a variety of computer vision tasks. Using deep neural networks to construct road marking detection systems is a practical solution. In this paper, we present an accurate and effective road marking detection system to handle seven common road markings. Our model is based on the R-FCN network framework, with the ResNet-18 model as backbone. SE blocks and data balancing strategies are also used to further improve the accuracy of the detection model. Our model has made a good trade-off between accuracy and speed, and achieved quite good results in our self-built road marking dataset.
Reading Experiences on Exploring Personal Reading Histories of English Study Program Learners of University of Riau

Learners have different historical reading experiences which can be analyzed from any perspectives. This study aimed to explore “the reading experiences of two successful English study program learners of the Faculty of Teacher Training and Education University of Riau in Extensive Reading course based on Exploring Personal Reading Histories”. The suitable design in answering the research question of this study is a Case Study of the two successful learners in Extensive Reading course. Based on the result of the analysis of this study, it was found that reading experiences of English study program learners of the Faculty of Teacher Training and Education University of Riau based on Exploring Personal Reading Histories is individual contexts (the facilities provided by parents and the involvement of teachers) reflecting on the process in becoming competent in each level. As a result, their experiences develop their interest to do activities of reading without instruction. It also indicates that this study contributes to reading theory, especially extensive reading relating with the other theories as well as pedagogy in teaching and learning Extensive Reading by implementing Exploring Personal Reading Histories at the beginning of the course. This finding contributes to theory of reading, especially extensive reading and to methodology in terms of the appropriate instruments and analysis for qualitative researches.
The Future of Data: Too Much Visualization, Too Little Understanding? An empirical investigation into the comprehension of data visualizations

Data visualization can be useful in enhancing and understanding the patterns and stories behind given data.For at least a decade, however, we have observed a disturbing trend of designers complicating and embellishing visualizations of essentially very simple data sets to the point of obscuring the essential information they were designed to convey.To gauge some of the effects of this practice on how well audiences comprehend the data underlying these types of visualizations, we conducted an experiment that entailed the selection of five examples from readily available and reputable published sources.These ranged from widely read newspapers and magazines to online news outlets to scholarly journals.We then developed simpler visualizations that utilized the iden tical data as those that we deemed to be overly complex, and randomly exposed subjects to either of these visualizations to assess their ability to effectively perceive the displayed data.The subjects were 72 junior and senior undergraduate students at a US landgrant university.We found that, on average, it took students longer to comprehend the complex versions, that they took longer to extract information from them, and that they made larger errors of interpretation as they did so.The experiment provided evidence that subjects eventually did learn to navigate the more complex versions of the data visualizations, but this took them significantly more time.This caused us to question whether the discipline of data visualization is trending toward a future that will challenge audiences to interpret too many complex visualizations of data that yield too little understanding.
Symbolic Formulae for Linear Mixed Models

A statistical model is a mathematical representation of an often simplified or idealised data-generating process. In this paper, we focus on a particular type of statistical model, called linear mixed models (LMMs), that is widely used in many disciplines e.g.~agriculture, ecology, econometrics, psychology. Mixed models, also commonly known as multi-level, nested, hierarchical or panel data models, incorporate a combination of fixed and random effects, with LMMs being a special case. The inclusion of random effects in particular gives LMMs considerable flexibility in accounting for many types of complex correlated structures often found in data. This flexibility, however, has given rise to a number of ways by which an end-user can specify the precise form of the LMM that they wish to fit in statistical software. In this paper, we review the software design for specification of the LMM (and its special case, the linear model), focusing in particular on the use of high-level symbolic model formulae and two popular but contrasting R-packages in lme4 and asreml.
Design and implementation of line follower and obstacle detection robot

&lt;p&gt;In this paper, we propose a method for a line follower robot based on the instantaneous computation of the radius of curvature of this line, using infrared line sensors. The number and layout of its sensors, as well as the method chosen, play an important role in the robot's response to the line, with the desired accuracy and speed. In addition, the robot must be equipped with an anti-collision system, using an ultrasonic distance sensor, to detect and avoid obstacles in several situations, especially at line crossovers, when other robots share a common complex line.&lt;/p&gt;
Enhancing National Security through Internet Broadcasting: benefits and limitations

The article is dedicated to the evaluation of the potential of Internet Broadcasting for the strengthening the national security system of the state. The importance of this problem is caused by the security priorities, which the stability of the political system of the state, life, health and welfare of the nation depend on. The author makes his theoretical conclusions on the basis of an empirical research conducted in three regions of Nigeria. This African country has many national security issues: religious conflicts, terrorism, banditry, kidnapping, separatism, including the Biafra Sovereign Movement in the Southeast, “Boko Haram” activity in the north and Niger Delta fighters, as well as starvation, impoverishment, communal strife, conflict between pastoralists and farmers, the weakness of the federal government, lack of medical services, unemployment and so forth. The hypothesis is that Internet broadcasting significantly contributes to the solving of these problems. Scientific tasks that are accomplished in the research are: a) determining of the extent to which Internet broadcasting can enhance national security in Nigeria; b) ascertain the benefits and c) investigate the limitations of using the Internet broadcasting for solving national security problems. A sample of 384 was drawn from three states of Enugu, Abia and Anambra through a multi-stage sampling technique. In the analysis of data for the study, SPSS version 22 was used while simple percentages, mean and standard deviation were used as statistical tools. The result of this study showed that 52% of the respondents reported that Internet broadcasting can promote national security to a large extent. The respondents mentioned better speed, accessibility, a wider reach than TV, usability as benefits of Internet broadcasting. On the other hand, the limitations of the Internet broadcasting are: Internet broadcasting cannot be accessed by people without Internet skills; expensive gadgets needed for access; relatively short term of gadget’s power etc. Analyzing conclusions of the research, the author reflects on the Technological Determinism theory, Safe Nigeria model and Nigeria’s national security objectives. Practical recommendations about the improvement of the national security system of Nigeria through the creation of conducive environment for Internet broadcasting are given.
Spoken Language Detection Experiments for Romanian

In this paper, we present spoken language detection experiments using Romanian, English and German corpora. The spoken language recognition algorithm is based on a long-short term neural network which maps a sequence of Mel-cepstral coefficients features to a vector space, followed by a multi-layer perceptron employed as a language classifier. Results are presented for different parameter configurations, for Romanian versus English and Romanian versus English versus German discrimination. In terms of accuracy, for the Romanian versus English system, the results were 92 % for the test set. For Romanian versus English versus German, the results were encouraging (73%), despite the limited sizes of the three corpora.
Welcome to the Multiverse: Law, Technology and Humans

Law, Technology and Humans aims for something different from the mainstream of technology law scholarship. Rather than repeating analysis born from the dominant narrative, it boldly presents itself as a portal to the multiverse of stories and methods through which to understand, dream, critique, build and live well in the technological present as it, with every planetary rotation, moves towards the technological future.
Peningkatan Motivasi dan Prestasi Belajar Menulis Teks Eksposisi Menggunakan Model Discokaku Dipadu Gambar Berseri di SMA Negeri 5 Magelang

This research was aimed to investigate students' motivation and their learning achievement in writing exposition texts. To seek the answer, we used discokaku teaching method and pictorial series. This study was carried out under classroom action research. We implemented three main stages of pre-cycle, cycle I, and cycle II, each cycle using three meetings. The participants were 10th graders of SMA Negeri 5 Magelang. The data were attained from test and non-test techniques. The test technique deployed (1) Kahoot! for assessing their understanding and (2) pictorial series for the writing test. For the non-test, this study adopted some qualitative instruments: journal, observation, interview, and documentation. The result shows that discokaku teaching method and pictorial series were able to improve students’ motivation from 23.47% to 33.87% and their achievement from 3.13% to 78.13% at the end of the cycle 2.
Study of blue shift in Iron (Fe) doped ZnO Nanoparticles at different Molarities by Wet Chemical Method

Iron (Fe) doped Zinc Oxide (ZnO) nanoparticle is a very important topic of study in the field of nanoscience and nanotechnology.We have prepared Iron doped ZnO nanoparticles by Wet chemical method.We report the optical properties of Iron (Fe) doped ZnO nanoparticles at different molarities (0.025M, 0.05M and 0.1M).The band gaps of all nanoparticles prepared at different molarities are calculated with the intercept on Y-axis of Tauc plot from UV absorption spectra using UV-Vis Absorption spectroscopy.It has been observed that band gap decreases as the molarity increases.At 0.025M, 0.05M and 0.1M band gap are found to be 4.9eV, 4.89eV and 2.7eV respectively.This reduction in band gap suggest that the band gap of prepared nanoparticles are shifted towards lower energy.This shifting in band gap the blue region gives the idea of blue shift quite well.This blue shift is dependent on the particle size of the nanoparticles.
Pose-Invariant Facial Expression Recognition Based on 3D Face Morphable Model and Domain Adversarial Learning

Pose is one of the most important factors affecting performance of face related recognition algorithms including facial expression recognition (FER). Traditionally, non-frontal FER is conducted by either performing face formalization or designing separate models for different poses. Different from those methods, we propose a one-stage FER approach by training a pose invariant deep convolutional network (DCNN) with the following novelties: First, we introduce the 3D face morphable model to reconstruct high fidelity 3D faces for data augmentation which increases the pose variety without losing expression information. Second, we employ domain adversarial learning to eliminate the influence of domain difference between real 2D face images and 3D synthetic face images at feature level, which realizes a one-stage deep FER approach that is robust to different face poses. Third, the proposed approach provides a solution for cross-domain problems involving data from different sources, which can be applied to other face related recognition problems. The method is validated using three FER datasets FER2013, multi-PIE and BU-3DFE; and it outperforms the current state-of-the-art methods.
Subject Review: Machine Learning and Deep Learning based Arabic Handwriting Recognition

In recent times, with the progress of artificial intelligence and machine learning, handwriting recognition becomes an active research area. Furthermore, many products of applications have provided handwriting recognition services in various languages with a low margin of errors. The Arabic language has its challenges which make it not easy comparing with other languages to develop error-free and practical Arabic handwriting recognition. Deep learning has promising results in this field. This paper surveys existing literature work for Arabic handwriting recognition in general and using deep learning in particular.
Using Structural Equation Modelling to study the influence of perceived usefulness and perceived compatibility on students’ attitudes towards using IPAD

In this research an attempt has been made to test the integration of the Technology Acceptance Model (TAM), user satisfaction, and perceived compatibility in the educational field.It particularly uses Structural Equation Modelling with various constructs to investigate students' acceptance of using IPAD as a technological tool in undergraduate math classes in a Middle Eastern University.The result agrees with the theoretical model, it shows the influence of user satisfaction, perceived usefulness and perceived compatibility on students' attitudes towards using IPAD.Finding of this research along with the ongoing research should encourage educators to put more emphasis on using technology in the learning process of mathematics.
Robust Nonnegative Matrix Factorization Based on Cosine Similarity Induced Metric

Nonnegative matrix factorization (NMF) is a low-rank decomposition based image representation method under the nonnegativity constraint. However, a lot of NMF based approaches utilize Frobenius-norm or KL-divergence as the metrics to model the loss functions. These metrics are not dilation-invariant and thus sensitive to the scale-change illuminations. To solve this problem, this paper proposes a novel robust NMF method (CSNMF) using cosine similarity induced metric, which is both rotation-invariant and dilation-invariant. The invariant properties are beneficial to improving the performance of our method. Based on cosine similarity induced metric and auxiliary function technique, the update rules of CSNMF are derived and theoretically shown to be convergent. Finally, we empirically evaluate the performance and convergence of the proposed CSNMF algorithm. Compared with the state-of-the-art NMF-based algorithms on face recognition, experimental results demonstrate that the proposed CSNMF method has superior performance and is more robust to the variation of illumination.
PREDIKSI BEBAN ENERGI LISTRIK APJ KOTA SEMARANG MENGGUNAKAN METODE RADIAL BASIS FUNCTION (RBF)

Electric power is one of the main needs of society today, ranging from household consumers to industry. The demand for electricity increases every year. So as to achieve adjustments between power generation and power demand, the electricity provider (PLN) must know the load needs or electricity demand for some time to come. There are many studies on the prediction of electricity loads in electricity, but they are not specific to each consumer sector. One of the predictions of this electrical load can be done using the Radial Basis Function Artificial Neural Network (ANN) method. This method uses training data learning from 2010 - 2017 as a reference data. Calculations with this method are based on empirical experience of electricity provider planning which is relatively difficult to do, especially in terms of corrections that need to be made to changes in load. This study specifically predicts the electricity load in the Semarang Rayon network service area in 2019-2024. The results of this Artificial Neural Network produce projected electricity demand needs in 2019-2024 with an average annual increase of 1.01% and peak load in 2019-2024. The highest peak load in 2024 and the dominating average is the household sector with an increase of 1% per year. The accuracy results of the Radial Basis Function model reached 95%.
Prior-based Domain Adaptive Object Detection for Adverse Weather Conditions.

Adverse weather conditions such as haze and rain corrupt the quality of captured images, which cause detection networks trained on clean images to perform poorly on these images. To address this issue, we propose an unsupervised prior-based domain adversarial object detection framework for adapting the detectors to hazy and rainy conditions. In particular, we use weather-specific prior knowledge obtained using the principles of image formation to define a novel prior-adversarial loss. The prior-adversarial loss used to train the adaptation process aims to reduce the weather-specific information in the features, thereby mitigating the effects of weather on the detection performance. Additionally, we introduce a set of residual feature recovery blocks in the object detection pipeline to de-distort the feature space, resulting in further improvements. Evaluations performed on various datasets (Foggy-Cityscapes, Rainy-Cityscapes, RTTS and UFDD) for rainy and hazy conditions demonstrates the effectiveness of the proposed approach.
Enhancing a Tangible Tabletop with Embedded-Technology Objects for Experiencing Deep Time

Abstract concepts are harder to understand because they do not provide a direct sensory reference as concrete concepts do. Tangible interfaces have been shown benefits to facilitate the learning of such concepts through the manipulation of physical objects. In this work, we investigate the use of TangiTime, an educational exhibit designed and constructed as a tangible tabletop enhanced with embedded-technology objects to explore the concept of deep time. In particular, we were interested in the role of embedded-technology objects to support engagement and learning. We describe the TangiTime design process and artifacts implementation. Also, we present the context in which the exhibit was put into usage and results. Our results indicate that interaction with embedded-technology objects creates new ways of experiencing tangible tabletops and more engagement with the theme of the exhibit.
Pedestrian Parsing by Joint Learning from Wholes and Parts

Parsing pedestrian into different semantic regions makes an important component in pedestrian analysis. In this paper, we propose a novel pedestrian parsing framework that explores the correlated and complementary information by jointly learning global and local features with Fully Connected Networks (FCNs). The proposed network contains two branches: A global-local branch and a main brahch. The global-local branch is learnt under both local and global supervisions, which can discover the discriminative and fine-grained features for assisting the feature learning of the main branch. Comparing with either global or local feature learning, our framework can capture both the contextual and detailed information well. Thus, the learned features are more reliable and discriminative. We also design a multi-scale atrous spatial pyramid pooling (multi-scale ASPP) to reduce the junction effect in local features especially in global-local fusion features. It also helps to capture image context at multiple scales thus parsing pedestrian robustly. We evaluate the proposed method on PPSS and PennFudan datasets and achieve the state-of-the-art performance.