Reviewerly/exHarmony huggingface dataset:
Webinterpret Submission to the WMT2019 Shared Task on Parallel Corpus Filtering

This document describes the participation of Webinterpret in the shared task on parallel corpus filtering at the Fourth Conference on Machine Translation (WMT 2019). Here, we describe the main characteristics of our approach and discuss the results obtained on the data sets published for the shared task.
Analysis of Information Technology Governance Using the COBIT 5 Framework (Case Study: E-Legal Drafting Legal Section of the Regional Secretariat of Salatiga City)

Information Technology (IT) governance is used to manage and optimize IT resources in supporting organizational goals. The Legal Section of the Regional Secretariat of Salatiga City as part of a government organization has built E-Legal Drafting information system to develop the functions of making regional legal products as well as the realization of e-government development in the legal field. The COBIT framework supports IT governance by providing work support to regulate IT alignment with the organization's business objectives. The results of this study are expected to show an overview of the implementation of IT governance in E-Legal Drafting information system from APO domain within the COBIT 5 framework, show the current system information level of capability and performance that is obtained from the measurement of capability levels, and also providing evaluation and recomendation based on the results of the gap analysis to help obtain the quality of information systems expected by the Legal Section of the Regional Secretariat of Salatiga City.
Visual decisions in the analysis of customers online shopping behavior

The analysis of the online customer shopping behavior is an important task nowadays, which allows maximizing the efficiency of advertising campaigns and increasing the return of investment for advertisers. The analysis results of online customer shopping behavior are usually reviewed and understood by a non-technical person; therefore the results must be displayed in the easiest possible way. The online shopping data is multidimensional and consists of both numerical and categorical data. In this paper, an approach has been proposed for the visual analysis of the online shopping data and their relevance. It integrates several multidimensional data visualization methods of different nature. The results of the visual analysis of numerical data are combined with the categorical data values. Based on the visualization results, the decisions on the advertising campaign could be taken in order to increase the return of investment and attract more customers to buy in the online e-shop.
Chameleon: A Language Model Adaptation Toolkit for Automatic Speech Recognition of Conversational Speech

Language model is a vital component in modern automatic speech recognition (ASR) systems.Since "one-size-fits-all" language model works suboptimally for conversational speeches, language model adaptation (LMA) is considered as a promising solution for solving this problem.In order to compare the state-of-the-art LMA techniques and systematically demonstrate their effect in conversational speech recognition, we develop a novel toolkit named Chameleon, which includes the state-of-the-art cache-based and topic-based LMA techniques.This demonstration does not only vividly visualize underlying working mechanisms of a variety of the state-of-the-art LMA models but also provide an interface for the user to customize the hyperparameters of them.With this demonstration, the audience can experience the effect of LMA in an interactive and real-time fashion.We wish this demonstration would inspire more research on better language model techniques for ASR.
JHU 2019 Robustness Task System Description

We describe the JHU submissions to the French-English, Japanese-English, and English-Japanese Robustness Task at WMT 2019.Our goal was to evaluate the performance of baseline systems on both the official noisy test set as well as news data, in order to ensure that performance gains in the latter did not come at the expense of general-domain performance.To this end, we built straightforward 6-layer Transformer models and experimented with a handful of variables including subword processing (FR-EN) and a handful of hyperparameters settings (JA↔EN).As expected, our systems performed reasonably.
COMPARATIVE ANALYSIS OF CLASSIFIERS FOR EDUCATION CASE STUDY

Recently, classification is becoming a very valuable tool where a large amount of data is used on a wide range of decisions for the education sector.Classification is a method that used to group data based on predetermined characteristics.It is utilized to classify the item as indicated by the features for the predefined set of classes.The main significance of classification is to classify data from large datasets to find patterns out of it.Nevertheless, it is very important to choose the best classification algorithm which is also called as the classifier.Therefore, this research aims to conduct comparative evaluation between four classifiers which are Deep Neural Network (DNN), Random Forest (RF), Support Vector Machine (SVM) and Decision Tree (DT).All these classifiers have its own efficiency and have an important role in identifying the set of populations based on the training datasets.To choose the best classifiers among the four classifiers, the classifiers performance is required to be evaluated based on the performance metrics.The performance metrics of these classifiers were determined using accuracy and sensitivity rates.This study used education case study on student's performance data for two subjects, Mathematics and Portuguese from two Portugal secondary schools and data on the student's knowledge of Electrical DC Machines subject.After comparing the accuracy and sensitivity rates, DNN has the highest accuracy and sensitivity rate of classification and can be used to further the educationbased research in future.
Easy First Relation Extraction with Information Redundancy

Many existing relation extraction (RE) models make decisions globally using integer linear programming (ILP).However, it is nontrivial to make use of integer linear programming as a blackbox solver for RE.Its cost of time and memory may become unacceptable with the increase of data scale, and redundant information needs to be encoded cautiously for ILP.In this paper, we propose an easy first approach for relation extraction with information redundancies, embedded in the results produced by local sentence level extractors, during which conflict decisions are resolved with domain and uniqueness constraints.Information redundancies are leveraged to support both easy first collective inference for easy decisions in the first stage and ILP for hard decisions in a subsequent stage.Experimental study shows that our approach improves the efficiency and accuracy of RE, and outperforms both ILP and neural network-based methods.
ciuupi: An R package for Computing Confidence Intervals that Utilize Uncertain Prior Information

We have created the R package ciuupi to compute confidence intervals that utilize uncertain prior information in linear regression.Unlike post-model-selection confidence intervals, the confidence interval that utilizes uncertain prior information (CIUUPI) implemented in this package has, to an excellent approximation, coverage probability throughout the parameter space that is very close to the desired minimum coverage probability.Furthermore, when the uncertain prior information is correct, the CIUUPI is, on average, shorter than the standard confidence interval constructed using the full linear regression model.In this paper we provide motivating examples of scenarios where the CIUUPI may be used.We then give a detailed description of this interval and the numerical constrained optimization method implemented in R to obtain it.Lastly, using a real data set as an illustrative example, we show how to use the functions in ciuupi.
Residual Dilation Based Feature Pyramid Network

To address the issue of multi-scale detection, current detectors usually generate hierarchical feature pyramid by a naive combination of top-down features with lateral features. Due to the limited effective receptive fields in the top-down pathway, the generated regions are only associated with the neighbor regions of the coarse feature maps, which is harmful for pyramidal feature generation. And considering the weak representation of simply merging of top-down and lateral pathways, the pyramidal feature maps with strong semantics are difficult to obtain. In this paper, we present the Residual Dilation based Feature Pyramid Network (RDFPN) to exploit the inherent correlation of regions in feature pyramid. The goal of RDFPN is to produce more appropriate hierarchical feature maps for multi-scale detection. With Residual-50 network in Faster R-CNN framework, RDFPN outperforms the original FPN on the challenging COCO dataset without bells and whistles.
Change Detection via Graph Matching and Multi-View Geometric Constraints

Change detection is a critical preprocessing step of visual perception with broad prospects. Its primary challenge is to identify all the meaningful changes from a target image to the source image, which is observed of the same scene and has a different perspective as well. A robust change detection method involving graph matching and geometric constraints is proposed in this paper. Maximum common sub-graph matching is applied for alleviating the risk of suboptimal results and geometric constraints are used to remove the possible mistaken results. Detection results in different real-world scenes with respect to considerable textural moved objects show that the proposed method is more robust than the state-of-the-art methods.
Hierarchy Response Learning for Neural Conversation Generation

The neural encoder-decoder models have shown great promise in neural conversation generation.However, they cannot perceive and express the intention effectively, and hence often generate dull and generic responses.Unlike past work that has focused on diversifying the output at word-level or discourse-level with a flat model to alleviate this problem, we propose a hierarchical generation model to capture the different levels of diversity using the conditional variational autoencoders.Specifically, a hierarchical response generation (HRG) framework is proposed to capture the conversation intention in a natural and coherent way.It has two modules, namely, an expression reconstruction model to capture the hierarchical correlation between expression and intention, and an expression attention model to effectively combine the expressions with contents.Finally, the training procedure of HRG is improved by introducing reconstruction loss.Experiment results show that our model can generate the responses with more appropriate content and expression.
Wacnet: Word Segmentation Guided Characters Aggregation Net for Scene Text Spotting With Arbitrary Shapes

In this paper, we propose an end-to-end trainable framework for scene text spotting which can handle text with arbitrary shapes. The proposed framework is called Word Segmentation Guided Characters Aggregation Net (WAC-Net), which consists of a shared convolutional backbone and two task-specific subnetworks. One subnetwork does word-level instance-aware segmentation (WSN) and the other does char-level detection and recognition (CDRN). The entire framework segments each word instance while detects and recognizes each character in one single forward pass. These two subnetworks are jointly trained by multi-task learning. At the inference stage, characters are aggregated into words guided by word instance segmentation results. Experiments are conducted on two datasets with arbitrary shapes, and the results demonstrate the effectiveness of the proposed method.
Translating Between Morphologically Rich Languages: An Arabic-to-Turkish Machine Translation System

This paper introduces the work on building a machine translation system for Arabic-to-Turkish in the news domain.Our work includes collecting parallel datasets in several ways for a new and low-resource language pair, building baseline systems with state-ofthe-art architectures and developing language specific algorithms for better translation.Parallel datasets are mainly collected three different ways; i) translating Arabic texts into Turkish by professional translators, ii) exploiting the web for open-source Arabic-Turkish parallel texts, iii) using back-translation.We performed preliminary experiments for Arabicto-Turkish machine translation with neural (Marian) machine translation tools with a novel morphologically motivated vocabulary reduction method.
WhatsApp Application in English Language Teaching (ELT) Context: Media to Describe People

Learning media is a vehicle and delivery of information or learning messages to students who can help teachers improve student achievement. Learning media always develops, along with technological developments. This study aims to analyze the function of learning media using WhatsApp in learning material describing people can support improving vocabulary in students that can be used in the learning process of teaching English in Senior High School. WhatsApp media can be used according to the level of student needs and material to be delivered. This type of research is a literature review by gathering sources of information and data from written documents in the form of articles, journals, and books. The conclusion of the study is the existence of the benefits of WhatsApp media that can be used by teachers for learning English in senior high school from simple material to complex material by utilizing technological developments and advancements.
An Improved Rao–Nam Cryptosystem Based on Fractional Order Hyperchaotic System and EDF–QC–LDPC

A Rao–Nam cryptosystem based on error correction code is proposed to provide both security and reliability. Since its security is drastically constrained by the limited error syndromes, in this paper, an improved Rao–Nam cryptosystem based on fractional order hyperchaotic system and Extended Difference Family–Quasi-Cyclic–Low-Density Parity-Check (EDF–QC–LDPC) codes is proposed to improve the security. A four-dimensional fractional order hyperchaotic system is constructed and is used to generate an excellent pseudorandom sequence. By replacing error syndromes with the pseudorandom sequence and permuting the coded message dynamically, the security of the Rao–Nam cryptosystem is enhanced greatly. The ability of the improved Rao–Nam cryptosystem against known attacks is analyzed and the error correction performance with different parameters is simulated. The results show that the proposed cryptosystem has a significant advantage of resisting the chosen-plaintext attack. Moreover, the proposed cryptosystem retains high capacity of error correction.
Set to Ordered Text: Generating Discharge Instructions from Medical Billing Codes

Litton J Kurisinkel, Nancy Chen. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Supporting Better Physical Activity in a Smart City: a Framework for Suggesting and Supervising Walking Paths

The increase of elderly population creates the need to promote healthy aging, with autonomy and independence, for preserving the functional capacity and quality of life as much as possible.To achieve this goal the recommendations include walking a certain amount of steps daily, given that the exact amount of steps changes with age, lesions, and chronic health conditions that can affect health and well-being.Understanding how much exercise is adequate for an individual requires specialized knowledge and training.It is important to avoid risks that include: (1) be too aggressive in exercising and increase injuries, create new ones, and later refrain in exercising more; or (2) be too conservative and in the long run have an activity level under each one's potential and below what is recommended.In this article is proposed and discussed a framework that aims to support people having an adequate level of activity for its particular condition and to do it without disrupting daily routines.The framework is supported by a software system built for monitoring well-being and physical activity in the context of Smart Cities.The system is composed by: (1) smartphone applications that interact with the end-user for showing possible exercises and walk routes, and that collects some relevant data; (2) a back-office application that collects and presents data obtained from the smartphone applications.The back-office application is designed for health professionals to follow users' progression over time, to recommend new exercises or to correct less optimal situations.The first tests with a system implementing this framework show that the solution is robust and able to be used in a large scale.
Unsupervised Cross-Domain Person Re-Identification: A New Framework

Although existing person Re-IDentification (ReID) methods have achieved great progress with large-scale labeled data, it is still hard to generalize to unseen scenarios without any la-beled person identities. To alleviate this problem, this paper proposes a new framework to take full advantage of the label information of source domain and the data distribution geometry of unlabeled target domain to improve the ReID performance in the unlabeled target domain. Instead of direct model transfer, the data transfer is first adopted where the identity preserving samples are generated from the labeled source domain to unlabeled target domain. Accordingly, a better initialized target domain adapted ReID model could be obtained with the generated samples. The fine-grained part-level features are then learned instead of global features to better mine new persons in the unlabeled target domain. Finally, the proposed framework iteratively updates the ReID model with the generated persons and the mined persons in last iteration, and explores new persons from the unlabeled target domain. The state-of-the-art experimental results are achieved on Market1501 and DukeMTMC-reID in terms of unsupervised cross-domain person ReID.
Decision Support System for Assignment of Conference Papers to Reviewers

Research conferences are held to share research progress and novel findings among scientists and encourage the growth of academic communities. Assigning papers to reviewers is the most critical and arduous task for conference organizers. Usually, conference committees must distribute hundreds of publications to reviewers in a short time. Moreover, other restrictions—such as a limited number of reviewers and avoiding conflict of interest—make the process of wholly distributed more difficult. Manually choosing reviewers can be an unfair and time-consuming process. In this study, we developed a support system for automatically assigning submitted papers to reviewers. For this, we extracted features from reviewer profiles and their networks to compute their quality and relevance with respect to the submitted papers. Experiments on a DBLP dataset showed that our system achieved more accurate results than other methods.
Tindak Tutur Guru dan Siswa dalam Pembelajaran Bahasa Indonesia

&lt;p&gt;&lt;em&gt;This research aims to describe the speech act of locussion, illocussion, and perlocussion in Bahasa Indonesia subject for students grade X SMAN 8 Kerinci. Speech act is a crucial aspect which is used in the teaching and learning process in the classroom. Therefore, it is important to concern the use of speech act done by both teachers and the students in order to accomplish the learning goals. The theories used to analize the speech act of the teachers and students in the learning process of Bahasa Indonesia subject in this research are proposed by Nadar (2008), Suadi (2014), Chaer (2010), Bonodari (20115), and Rehardi (2005). The type of this research is qualitative research. The subject of the research are teachers and students grade X in SMaN 8 Kerinci in the process of teaching and learning in Bahasa Indonesia subject. The technique of data collection are namely listening freely and speaking involved, recording techinque, and writing down technique. The technique of data analysis is equal pragmatics. The result of the research can be described as follow: it is found the speech act in form of locussion, illocussion, and perlocussion. The speech act of locussion includes the forms of news, questions forms and commands forms. The speech act of illocussion includes assertive illocussion, directive, expressive, commisive, and declarative. The speech act of perlocussion that was found is giving influences&lt;/em&gt;&lt;em&gt;.&lt;/em&gt;&lt;/p&gt;
Assessing Incrementality in Sequence-to-Sequence Models

Since their inception, encoder-decoder models have successfully been applied to a wide array of problems in computational linguistics.The most recent successes are predominantly due to the use of different variations of attention mechanisms, but their cognitive plausibility is questionable.In particular, because past representations can be revisited at any point in time, attention-centric methods seem to lack an incentive to build up incrementally more informative representations of incoming sentences.This way of processing stands in stark contrast with the way in which humans are believed to process language: continuously and rapidly integrating new information as it is encountered.In this work, we propose three novel metrics to assess the behavior of RNNs with and without an attention mechanism and identify key differences in the way the different model types process sentences.
Partners in Crime: Multi-view Sequential Inference for Movie Understanding

Multi-view learning algorithms are powerful representation learning tools, often exploited in the context of multimodal problems.However, for problems requiring inference at the token-level of a sequence (that is, a separate prediction must be made for every time step), it is often the case that single-view systems are used, or that more than one views are fused in a simple manner.We describe an incremental neural architecture paired with a novel training objective for incremental inference.The network operates on multi-view data.We demonstrate the effectiveness of our approach on the problem of predicting perpetrators in crime drama series, for which our model significantly outperforms previous work and strong baselines.Moreover, we introduce two tasks, crime case and speaker type tagging, that contribute to movie understanding and demonstrate the effectiveness of our model on them. 1
Use of Natural Language Processing to Identify Inappropriate Content in Text

The quick development of communication through new technology media such as social networks and mobile phones has improved our lives. However, this also produces collateral problems such as the presence of insults and abusive comments. In this work, we address the problem of detecting violent content on text documents using Natural Language Processing techniques. Following an approach based on Machine Learning techniques, we have trained six models resulting from the combinations of two text encoders, Term Frequency-Inverse Document Frequency and Bag of Words, together with three classifiers: Logistic Regression, Support Vector Machines and Naïve Bayes. We have also assessed StarSpace, a Deep Learning approach proposed by Facebook and configured to use a Hit@1 accuracy. We evaluated these seven alternatives in two publicly available datasets from the Wikipedia Detox Project: Attack and Aggression. StarSpace achieved an accuracy of 0.938 and 0.937 in these datasets, respectively, being the algorithm recommended to detect violent content on text documents among the alternatives evaluated.