Reviewerly/exHarmony huggingface dataset:
PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents

Neural Machine Translation (NMT) has shown drastic improvement in its quality when translating clean input, such as text from the news domain.However, existing studies suggest that NMT still struggles with certain kinds of input with considerable noise, such as User-Generated Contents (UGC) on the Internet.To make better use of NMT for cross-cultural communication, one of the most promising directions is to develop a model that correctly handles these expressions.Though its importance has been recognized, it is still not clear as to what creates the great gap in performance between the translation of clean input and that of UGC.To answer the question, we present a new dataset, PheMT, for evaluating the robustness of MT systems against specific linguistic phenomena in Japanese-English translation.Our experiments with the created dataset revealed that not only our in-house models but even widely used off-the-shelf systems are greatly disturbed by the presence of certain phenomena.
Using Adobe Flash-Based Game to Educate Children about Covid-19

&lt;p class="BodytextMaJER"&gt;Some protocols published by WHO during this outbreak are to keep safe distance with minimum 1 meter, wear face masks especially in public places. Other health protocols such as wash hands using soap or hand sanitizer or have shower after being at home from work or other places are also being campaigned by government. However, most information provided used difficult language and hard to understand by children. This study therefore attempts to combine all of those backgrounds by creating a digital game related to Covid-19 which hopefully will provide positive contribution during this hard time. Furthermore, this game attempts to cover the right for children to access online gaming while keep educating them and protecting them from negative influences. This research employed Doppler Interactive Game Design Life Cycle as proposed by Joshua McGracth which basically consists of 6 iterative phases (Ramadan &amp;amp; Widyani, 2013), but here stopped at the testing process. Software used to develop this game were Adobe Flash and Adobe Premiere. The development if this game is also assessed appropriate by parents and English teachers in terms of the layout, the language or terms appropriateness, and the effectiveness.&lt;/p&gt;
THE IMPLEMENTATION OF YOUTUBE IN TEACHING VOCABULARY FOR YOUNG LEARNERS

Vocabulary has become a crucial aspect of learning foreign language. For young learners, the first words that they acquire could be like profound basis for a better learning of children. Related to the importance of vocabulary for young learners the teachers have to provide a new interesting teaching aid such as YouTube. The aim of this study is to find out the the differences between students who are taught by using YouTube and those who are not and to find out students’ perception of using YouTube in teaching vocabulary. This study employed quantitative and qualitative research design. The result of the study showed that the use of YouTube in this study is effective in teaching vocabulary for young learners and students who are taught by using YouTube are master in vocabulary. Furthermore, from the classroom observation and questionnaire, the use of YouTube can motivate students to learn vocabulary and the students have positive perception of using YouTube in teaching vocabulary for young learners
Pre-Service EFL Teachers’ Digital Literacy and Factors Affecting Digital Literacy Development

This study was intended to investigate pre-service EFL teachers’ digital literacy skills and factors affecting them in developing their digital literacy skills. Respondents participating in this research were the English Education Study Program students of a university in South Sumatra Province. They were in the fourth-year of study. Research data were collected through questionnaires. Research findings revealed that pre-service EFL teachers’ digital literacy was categorized above acceptable level. The main factors inhibiting them from developing digital literacy competence were a limited number of computers with online access on campus, no ICT training provided by the university for them, less practice of digital technology in teaching and learning activities, lack of budget to access the internet outside campus. Sustainable availability of digital resources at the campus, regular training of ICT on campus, and persistent integration of digital technologies in language learning seemed to be important develop their digital literacy skills.
SENTIMENT ANALYSIS ON TWITTER OF PSBB EFFECT USING MACHINE LEARNING

A collection of tweets from Twitter users about PSBB can be used as sentiment analysis. The data obtained is processed using data mining techniques (data mining), in which there is a process of mining the text, tokenize, transformation, classification, stem, etc. Then calculated into three different algorithms to be compared, the algorithm used is the Decision Tree, K-NN, and Naïve Bayes Classifier to find the best accuracy. Rapidminer application is also used to facilitate writers in processing data. The highest results from this study were the Decision Tree algorithm with an accuracy of 83.3%, precision 79%, and recall 87.17%.
Variation in Coreference Strategies across Genres and Production Media

In response to (i) inconclusive results in the literature as to the properties of coreference chains in written versus spoken language, and (ii) a general lack of work on automatic coreference resolution on both spoken language and social media, we undertake a corpus study involving the various genre sections of Ontonotes, the Switchboard corpus, and a corpus of Twitter conversations.Using a set of measures that previously have been applied individually to different data sets, we find fairly clear patterns of "behavior" for the different genres/media.Besides their role for psycholinguistic investigation (why do we employ different coreference strategies when we write or speak) and for the placement of Twitter in the spoken-written continuum, we see our results as a contribution to approaching genre-/media-specific coreference resolution.
Reinforcement Learning-based Product Delivery Frequency Control

Frequency control is an important problem in modern recommender systems. It dictates the delivery frequency of recommendations to maintain product quality and efficiency. For example, the frequency of delivering promotional notifications impacts daily metrics as well as the infrastructure resource consumption (e.g. CPU and memory usage). There remain open questions on what objective we should optimize to represent business values in the long term best, and how we should balance between daily metrics and resource consumption in a dynamically fluctuating environment. We propose a personalized methodology for the frequency control problem, which combines long-term value optimization using reinforcement learning (RL) with a robust volume control technique we termed "Effective Factor". We demonstrate statistically significant improvement in daily metrics and resource efficiency by our method in several notification applications at a scale of billions of users. To our best knowledge, our study represents the first deep RL application on the frequency control problem at such an industrial scale.
Generating Equation by Utilizing Operators : GEO model

Math word problem solving is an emerging research topic in Natural Language Processing.Recently, to address the math word problem solving task, researchers have applied the encoderdecoder architecture, which is mainly used in machine translation tasks.The state-of-the-art neural models use hand-crafted features and are based on generation methods.In this paper, we propose the GEO (Generation of Equations by utilizing Operators) model that does not use handcrafted features and addresses two issues that are present in existing neural models: 1. missing domain-specific knowledge features and 2. losing encoder-level knowledge.To address missing domain-specific feature issue, we designed two auxiliary tasks: operation group difference prediction and implicit pair prediction.To address losing encoder-level knowledge issue, we added an Operation Feature Feed Forward (OP3F) layer.Experimental results showed that the GEO model outperformed existing state-of-the-art models on two datasets, 85.1% in MAWPS, and 62.5% in DRAW-1K, and reached comparable performance of 82.1% in ALG514 dataset.
A Web-Based Approach for Visualizing Interactive Decision Maps

This research expands the applicability of the Feasible Goals (FGoal) Pareto frontier multiple criteria method to display the Edgeworth–Pareto hull using interactive decision maps (IDMs). Emphasis is placed upon the development of a communication architecture to display the Pareto frontiers, which includes a client device, a web server, and a dedicated computation server implemented with sockets. A standalone application on the latter processes client-server requests and responses to display updated information on the client. Specifically, the dedicated computation server is responsible for calculating the information needed to generate the Edgeworth–Pareto hull. This is delivered to the web server to generate the IDM to be displayed on the client device. The key innovation of this work is a tool that is developed to aid decision-makers with a network-based computational architecture that includes a computational server constantly in communication with a web server for fast responses to client requests to represent IDMs. Results show that this innovation avoids time-consuming communication, and this approach to represent IDMs on the web facilitates collaboration among decision-makers because they can analyze several complex problems in different browser windows and decide which problem and solution better correspond to their aims.
Semantic Web Applications: Current Trends in Datasets, Tools and Technologies’ Development for Linked Open Data

We report a survey on the actual state of the art about semantic web and its applications.Semantic Web plays a major role in integrating data, especially open data, publicly available on the Internet.We reviewed scientifically research papers, study cases, web sites and specialty books in order to discuss the main applicative areas, especially in the field of governmental use, the main technologies and the architectures involved.Both quantitative and qualitative analyses were carried out on the data, which related to 1460 ontologies belonging to Linked Open Data Cloud.The second analysis was on the content of scientific articles belonging to Clarivate Analytics, Scopus and Google Scholar databases.We identified and analyzed 84941 articles written on the subject of ontology, from which computer science is represented by 36264 articles.The results of our research proved that semantic web technologies are an important tool for describing and integrating data and an important component in the data layer of any intelligent application.This study contributed to the mainstream of the research literature by presenting the applicative areas of semantic web and semantic web applications' development tools, architectures, and methodology.
UAICS at SemEval-2020 Task 4: Using a Bidirectional Transformer for Task a

Commonsense Validation and Explanation has been a difficult task for machines since the dawn of computing.Although very trivial to humans it poses a high complexity for machines due to the necessity of inference over a pre-existing knowledge base.In order to try and solve this problem the SemEval 2020 Task 4 -"Commonsense Validation and Explanation (ComVE)" aims to evaluate systems capable of multiple stages of ComVE.The challenge includes 3 tasks (A, B and C), each with it's own requirements.Our team participated only in task A which required selecting the statement that made the least sense.We choose to use a bidirectional transformer in order to solve the challenge, this paper presents the details of our method, runs and result.
Nilai-nilai Pendidikan dalam Novel Dhadhung Kepuntir karya Tulus S. (Pendekatan Sosiologi Sastra Swingewood)

The purpose of this research is to find social reflection on the novel Dhadhung Kepuntir by Tulus S., and the educational values ​​in the novel. This research is a qualitative descriptive study through the sociology approach of Swingewood literature. Content analysis and descriptive analysis were used as data analysis techniques. The data source used is the novel Dhadhung Kepuntir by Tulus S. The result of this research is that the novel Dhadhung Kepuntir reflects the social facts of Javanese society. This can be seen from the depiction; the languages, ethnic, the place, the socio-cultural background in the novel is like a description of finding a job. In addition, there are various educational values ​​that are reflected in the novel Dhadhung Kepuntir by Tulus S., including: persistence (patience, never giving up, working hard), caring values ​​(kinship, harmony, respect), religious values ​​(gratitude, noble, sincere). The depiction of the character Darmo who is always persistent in changing the fate to get the better life, can be inspiration for the younger generation to achieve their dreams.&#x0D; Keywords: education value; sociology of literature; reflection
Complexity in Data-Driven Fuzzy Inference Systems: Survey, Classification and Perspective

Nowadays, data-driven fuzzy inference systems (FIS) have become popular to solve different vague, imprecise, and uncertain problems in various application domains.However, plenty of authors have identified different challenges and issues of FIS development because of its complexity that also influences FIS quality attributes.Still, there is no common agreement on a systematic view of these complexity issues and their relationship to quality attributes.In this paper, we present a systematic literature review of 1340 scientific papers published between 1991 and 2019 on the topic of FIS complexity issues.The obtained results were systematized and classified according to the complexity issues as computational complexity, complexity of fuzzy rules, complexity of membership functions, data complexity, and knowledge representation complexity.Further, the current research was extended by extracting FIS quality attributes related to the found complexity issues.The key, but not all, FIS quality attributes found are performance, accuracy, efficiency, and interpretability.
UJNLP at SemEval-2020 Task 12: Detecting Offensive Language Using Bidirectional Transformers

In this paper, we built several pre-trained models to participate SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media.In the common task of Offensive Language Identification in Social Media, pre-trained models such as Bidirectional Encoder Representation from Transformer (BERT) have achieved good results.We preprocess the dataset by the language habits of users in social network.Considering the data imbalance in OffensEval, we screened the newly provided machine annotation samples to construct a new dataset.We use the dataset to fine-tune the Robustly Optimized BERT Pretraining Approach (RoBERTa).For the English subtask B, we adopted the method of adding Auxiliary Sentences (AS) to transform the single-sentence classification task into a relationship recognition task between sentences.Our team UJNLP wins the ranking 16 th of 85 in English subtask A (Offensive language identification) .
Modern digital distance learning technologies: challenges of future teacher training

The article deals with the problem of using digital distance learning technologies, intensified in the current situation because of the coronavirus pandemic.Today's societal challenges require teachers with innovative thinking, the ability to adapt to different professional conditions, the ability to respond to innovations and use them in digital communication with students online.There is a necessity to train future teachers to use digital technologies and tools in the educational process. The article presents the results of an empirical study of this problem.The survey method revealed the state of the outlined problem in educational practice, secondary school teachers’ and university educators’ understanding of problems and challenges regarding the usage of digital resources and distance learning tools.The sample included different age categories of respondents: Boomer Generation, Generation X and Millenials (Generation Y). It was found that respondents have difficulties in using didactic knowledge in distance learning (36.4%),creation of moral and psychological comfort (36.4%), organization and stimulation of activity (25.5%) and effective online communication (21.8%).It wasrevealed that in the conditions of the pandemic a significant part of practitioner-teachers (53.8%) faced the problem of insufficient technical support of the educational institution;17.3% of respondents are dissatisfied with the level of their digital literacy and are looking for ways to increase professional competence in this area. The authors of the article proposedigital tools and applications for distance learning, as well as outline ways to prepare teachers for the effective use of digital learning technologies. Organizational forms of distance learning are presented, such as: online courses, online counseling, hackathons, webinars, online trainings, interactive educational platforms, virtual / remote laboratories, as well as visits to international science museums and virtual technology parks, creation of communication platforms taking into account scientific interests, etc.
Improving Deep Interactive Evolution with a Style-Based Generator for Artistic Expression and Creative Exploration

Deep interactive evolution (DeepIE) combines the capacity of interactive evolutionary computation (IEC) to capture a user’s preference with the domain-specific robustness of a trained generative adversarial network (GAN) generator, allowing the user to control the GAN output through evolutionary exploration of the latent space. However, the traditional GAN latent space presents feature entanglement, which limits the practicability of possible applications of DeepIE. In this paper, we implement DeepIE within a style-based generator from a StyleGAN model trained on the WikiArt dataset and propose StyleIE, a variation of DeepIE that takes advantage of the secondary disentangled latent space in the style-based generator. We performed two AB/BA crossover user tests that compared the performance of DeepIE against StyleIE for art generation. Self-rated evaluations of the performance were collected through a questionnaire. Findings from the tests suggest that StyleIE and DeepIE perform equally in tasks with open-ended goals with relaxed constraints, but StyleIE performs better in close-ended and more constrained tasks.
Diverse and Non-redundant Answer Set Extraction on Community QA based on DPPs

In community-based question answering (CQA) platforms, it takes time for a user to get useful information from among many answers.Although one solution is an answer ranking method, the user still needs to read through the top-ranked answers carefully.This paper proposes a new task of selecting a diverse and non-redundant answer set rather than ranking the answers.Our method is based on determinantal point processes (DPPs), and it calculates the answer importance and similarity between answers by using BERT.We built a dataset focusing on a Japanese CQA site, and the experiments on this dataset demonstrated that the proposed method outperformed several baseline methods.
Yseop at SemEval-2020 Task 5: Cascaded BERT Language Model for Counterfactual Statement Analysis

In this paper, we explore strategies to detect and evaluate counterfactual sentences. We describe our system for SemEval-2020 Task 5: Modeling Causal Reasoning in Language: Detecting Counterfactuals. We use a BERT base model for the classification task and build a hybrid BERT Multi-Layer Perceptron system to handle the sequence identification task. Our experiments show that while introducing syntactic and semantic features does little in improving the system in the classification task, using these types of features as cascaded linear inputs to fine-tune the sequence-delimiting ability of the model ensures it outperforms other similar-purpose complex systems like BiLSTM-CRF in the second task. Our system achieves an F1 score of 85.00% in Task 1 and 83.90% in Task 2.
Voice@SRIB at SemEval-2020 Tasks 9 and 12: Stacked Ensemblingmethod for Sentiment and Offensiveness detection in Social Media

In social-media platforms such as Twitter, Facebook, and Reddit, people prefer to use code-mixed language such as Spanish-English, Hindi-English to express their opinions.In this paper, we describe different models we used, using the external dataset to train embeddings, ensembling methods for Sentimix, and OffensEval tasks.The use of pre-trained embeddings usually helps in multiple tasks such as sentence classification, and machine translation.In this experiment, we have used our trained code-mixed embeddings and twitter pre-trained embeddings to SemEval tasks.We evaluate our models on macro F1-score, precision, accuracy, and recall on the datasets.We intend to show that hyper-parameter tuning and data pre-processing steps help a lot in improving the scores.In our experiments, we are able to achieve 0.886 F1-Macro on OffenEval Greek language subtask post-evaluation, whereas the highest is 0.852 during the Evaluation Period.We stood third in Spanglish competition with our best F1-score of 0.756.
Dynamic game semantics

Abstract The present work achieves a mathematical, in particular syntax-independent , formulation of dynamics and intensionality of computation in terms of games and strategies . Specifically, we give game semantics of a higher-order programming language that distinguishes programmes with the same value yet different algorithms (or intensionality) and the hiding operation on strategies that precisely corresponds to the (small-step) operational semantics (or dynamics) of the language. Categorically, our games and strategies give rise to a cartesian closed bicategory , and our game semantics forms an instance of a bicategorical generalisation of the standard interpretation of functional programming languages in cartesian closed categories. This work is intended to be a step towards a mathematical foundation of intensional and dynamic aspects of logic and computation; it should be applicable to a wide range of logics and computations.
MALAYSIA’S LEGAL RESPONSE TO TACKLING THE CRIME OF ONLINE CHILD PORNOGRAPHY

Child pornography is not a novel crime. For many years, it has been a prevalent concern but since the availability of the internet, it has become more universal and pervasive, as cyberspace provides offenders with greater accessibility to victimize children. In response to this, numerous countries around the world, including Malaysia have implemented and enhanced regulations and policies to accord better protection to children from the crime. The aim of this paper is to highlight Malaysia’s legal response and its efficacy in repressing the crime of online child pornography. Accordingly, the paper uses a doctrinal approach with content analysis to consider four issues. Firstly, the international and regional legal frameworks addressing online child pornography will be explored. Secondly, the paper provides an overview of the increasing threat of the crime in Malaysia. Subsequently, the legislative intervention taken by the nation to curb the crime is analysed, accompanied by an assessment on whether the existing law is consistent with international conventions. The overall finding reveals that the Malaysian legal mechanism has been substantially reformed to safeguard children in the country from online child pornography, especially after the enactment of the Sexual Offences Against Children Act 2017.