Reviewerly/exHarmony huggingface dataset:
Teaching English for Specific Purposes (ESP) to the Students in English Language Teaching (ELT)

This article will provide 1) general overview and course design of English for Specific Purposes in the field of ELT (English Language Teaching), 2) the role of teacher and student in English for Specific Purposes (ESP), and 3) the difficulties related to teacher, student, environment and others in teaching English for Specific Purposes (ESP). In the field of English Language Teaching (ELT), English for Specific Purposes (ESP) concerns the specific English language needs of the target learners/students. It refers to teaching a specific genre of English for students with specific goals which is oriented and focused on English teaching and learning. ESP is designed and developed based on an assessment of purposes and needs and the activities for which English is needed. There are many teacher’s roles, such as asking to organize courses, setting the learning objectives, establishing a positive learning environment and evaluating the students' progress. While, the learners are related to a specific interest in learning, subject matter knowledge, and well-built learning strategies. In the implementation of ELT, there are any difficulties or problems related to the teacher in teaching ESP, such as the low quality of lectures and textbooks, teachers’ improper qualification and teaching methods and lack of a theoretical framework of teaching ESP. Difficulties related to the students, such as demographic characteristics and demands of learning ESP, English proficiency, differences between different languages, lack of vocabulary, depending on the dictionary and lack of skills in using dictionary especially ESP terms. While, the difficulties related to the environment and others are lack of teaching materials, classes with a too large student number, and heavily focused on the examination.
Evolutionary Computing Assisted Visually- Imperceptible Hybrid Cryptography and Steganography Model for Secure Data Communication over Cloud Environment

The exponential growth of communication technologies and related application environments has broadened the cloud computing ecosystem horizon to meet major communication needs.However, in-parallel upsurge in online attacks, security breaches or allied intrusion events has alarmed industries to ensure optimal data security.Unlike text data transmission, image, or other multimedia communication over the cloud requires computational efficiency, imperceptibility, etc. to meet attack-resilient transmission.Amongst the major available security systems, the combination of cryptosystems and steganography has been identified as an augmented security model for data transmission.However, it demands enhancement in both stages to meet cloud-specific communication efficiency.Considering it as motivation, in this paper an efficient Visually Imperceptible Hybrid Crypto-Steganography (VIHCS) model is developed using Hybrid Cryptosystems followed by Adaptive Genetic Algorithm assisted Least Significant Bit (LSB) embedding process.We developed a novel Hybrid Cryptosystem by strategically applying Advanced Encryption Standard (AES) and Rivest-Shamir-Adleman (RSA) algorithms to secure secret data to be embedded in a cover image.In addition, the use of the Adaptive Genetic Algorithm based Optimal Pixel Adjustment (AGA-OPAP) strengthened the Least Significant Bit embedding while retaining the best possible image quality and visual imperceptibility.The proposed model achieved higher security, high embedding capacity as well as image quality, which is vital for cloud communication.To perform LSB embedding we applied 2D-Discrete Wavelet Transform (2D-DWT-2L) method with 8×8 dimensional blockwise embedding.It helps to achieve better embedding efficiency in conjunction with the AGA-OPAP model.Simulation results and respective visio-statistical assessment revealed that the proposed VIHCS model can accomplish better performance and reliable secure data communication over the cloud environment.Thus, the VIHCS model achieves maximum possible imperceptibility and hence can avoid attacks-such as steganalysis based attack or RS-attacks.
Classification with Strategically Withheld Data

Machine learning techniques can be useful in applications such as credit approval and college admission. However, to be classified more favorably in such contexts, an agent may decide to strategically withhold some of her features, such as bad test scores. This is a missing data problem with a twist: which data is missing depends on the chosen classifier, because the specific classifier is what may create the incentive to withhold certain feature values. We address the problem of training classifiers that are robust to this behavior. We design three classification methods: MINCUT, Hill-Climbing (HC) and Incentive-Compatible Logistic Regression (IC-LR). We show that MINCUT is optimal when the true distribution of data is fully known. However, it can produce complex decision boundaries, and hence be prone to overfitting in some cases. Based on a characterization of truthful classifiers (i.e., those that give no incentive to strategically hide features), we devise a simpler alternative called HC which consists of a hierarchical ensemble of out-of-the-box classifiers, trained using a specialized hill-climbing procedure which we show to be convergent. For several reasons, MINCUT and HC are not effective in utilizing a large number of complementarily informative features. To this end, we present IC-LR, a modification of Logistic Regression that removes the incentive to strategically drop features. We also show that our algorithms perform well in experiments on real-world data sets, and present insights into their relative performance in different settings.
From Sentiment Annotations to Sentiment Prediction through Discourse Augmentation

Sentiment analysis, especially for long documents, plausibly requires methods capturing complex linguistics structures.To accommodate this, we propose a novel framework to exploit task-related discourse for the task of sentiment analysis.More specifically, we are combining the largescale, sentiment-dependent MEGA-DT treebank with a novel neural architecture for sentiment prediction, based on a hybrid TreeLSTM hierarchical attention model.Experiments show that our framework using sentiment-related discourse augmentations for sentiment prediction enhances the overall performance for long documents, even beyond previous approaches using well-established discourse parsers trained on human annotated data.We show that a simple ensemble approach can further enhance performance by selectively using discourse, depending on the document length.
SEMANTIC GRAPH BASED TERM EXPANSION FOR SENTENCE-LEVEL SENTIMENT ANALYSIS

The semantic orientation (also referred to as prior polarity) of a word plays an important role in automatic sentence-level sentiment analysis. Several approaches have been proposed wherein a lexicon of words marked with their polarities is exploited to infer the meaning of sentences. However, relying on prior word polarity may produce inaccurate decisions. This is because we may find negative-sentence sentiments that include words with positive prior polarities or vice versa. In this article, we propose an approach to sentence-level sentiment analysis that exploits knowledge encoded in heavy-weight semantic graphs to assist in discovering the meaning of a word in the context of the sentence where it appears. In this context, we build contextual semantic networks for indexing sentences and expand them with semantically/lexically-relevant terms in an attempt to disambiguate the meanings of word mentions in sentences. In order to verify the effectiveness of the proposed approach, we have developed a prototype system using a real-world dataset that contains 46830 sentiment sentences along with a gold-standard that comprises 10000 movie reviews that are labelled under five sentiment categories (very negative, negative, neutral, positive, very positive). Findings indicate that enriching the semantic graphs of sentiment sentences with NOUN-based synonyms and hypernyms has improved the overall quality of baseline sentiment analysis techniques.
Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation

Cross-lingual Machine Reading Comprehension (CLMRC) remains a challenging problem due to the lack of large-scale annotated datasets in low-source languages, such as Arabic, Hindi, and Vietnamese.Many previous approaches use translation data by translating from a rich-source language, such as English, to low-source languages as auxiliary supervision.However, how to effectively leverage translation data and reduce the impact of noise introduced by translation remains onerous.In this paper, we tackle this challenge and enhance the cross-lingual transferring performance by a novel augmentation approach named Language Branch Machine Reading Comprehension (LBMRC).A language branch is a group of passages in one single language paired with questions in all target languages.We train multiple machine reading comprehension (MRC) models proficient in individual language based on LBMRC.Then, we devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages.Combining the LBMRC and multilingual distillation can be more robust to the data noises, therefore, improving the model's cross-lingual ability.Meanwhile, the produced single multilingual model can apply to all target languages, which saves the cost of training, inference, and maintenance for multiple models.Extensive experiments on two CLMRC benchmarks clearly show the effectiveness of our proposed method.
PRIORITIZED GA-PSO ALGORITHM FOR EFFICIENT RESOURCE ALLOCATION IN FOG COMPUTING

Everyone is connected to internet these days and internet plays a vital role in daily lives of humans.Ubiquitous devices like smart phones makes internet accessible anywhere.Key issue into day's computing environment is how to efficiently address the demands of billions of internet users.In computing environment, extension of cloud computing has been introduced, named as Fog computing.Fog computing provides services in the proximity of end user with very low latency.It is an intermediate layer between cloud and IoT devices.Fog layer's main objective is to reduce the delay and improve the response time for user's request.To use the Fog Computing effectively, the resources should be allocated in an efficient way, while catering to the user requests.In this paper, a hybrid Prioritized Genetic Particle Swarm Optimization (P-GA-PSO) algorithm has been proposed for efficient resource allocation in fog computing.This proposed algorithm allocates tasks to the resources efficiently, consequently reducing delay, waiting time and energy consumption by 8.73%, 22.65% and 17.81% respectively as well as improving resource utilization by 0.54% in comparison to GA.Similarly, the proposed algorithm when compared with Round Robin algorithm showed reduced delay, waiting time and energy consumption by 3.90%, 21.99% and 1.68% respectively as well as improving resource utilization by 12.51%.Further, the quantitative analysis showed that the proposed algorithm performs better than GA and round-robin algorithms and moves towards optimal solutions faster than these algorithms.
The Evaluation of Accuracy Performance in an Enhanced Embedded Feature Selection for Unstructured Text Classification

Text documents are unstructured and high dimensional. Effective feature selection is required to select the most important and significant feature from the sparse feature space. Thus, this paper proposed an embedded feature selection technique based on Term Frequency-Inverse Document Frequency (TF-IDF) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) for unstructured and high dimensional text classificationhis technique has the ability to measure the featureâ€™s importance in a high-dimensional text document. In addition, it aims to increase the efficiency of the feature selection. Hence, obtaining a promising text classification accuracy. TF-IDF act as a filter approach which measures features importance of the text documents at the first stage. SVM-RFE utilized a backward feature elimination scheme to recursively remove insignificant features from the filtered feature subsets at the second stage. This research executes sets of experiments using a text document retrieved from a benchmark repository comprising a collection of Twitter posts. Pre-processing processes are applied to extract relevant features. After that, the pre-processed features are divided into training and testing datasets. Next, feature selection is implemented on the training dataset by calculating the TF-IDF score for each feature. SVM-RFE is applied for feature ranking as the next feature selection step. Only top-rank features will be selected for text classification using the SVM classifier. Based on the experiments, it shows that the proposed technique able to achieve 98% accuracy that outperformed other existing techniques. In conclusion, the proposed technique able to select the significant features in the unstructured and high dimensional text document.
Strategi Komunikasi Dosen Dan Mahasiswa Dalam Meningkatkan Kualitas Pembelajaran Daring Selama Pandemic Covid-19

The purpose of this study was to analyze the communication strategies of lecturers and students in overcoming obstacles and boredom in online learning in the Communication Theory course. The research approach is qualitative with the case study method, the informant is a lecturer and 45 students. The results showed that during online lectures students tended to feel bored and get a culture shock in the learning process. To overcome this, the lecturer implements two-way communication, giving students the opportunity to respond either in the form of questions, discussions, and comments on what is conveyed by the lecturer. Lecturers position themselves as motivators by promoting polite communication, being able to be good listeners, and being the first to take the initiative, so that students dare to express opinions actively. it also needs to be the attention of lecturers and students to be active, creative and innovative. The right communication strategy is proven to be able to eliminate boredom in online lectures which in turn can improve the quality of learning.
Conception: Multilingually-Enhanced, Human-Readable Concept Vector Representations

To date, the most successful word, word sense, and concept modelling techniques have used large corpora and knowledge resources to produce dense vector representations that capture semantic similarities in a relatively low-dimensional space.Most current approaches, however, suffer from a monolingual bias, with their strength depending on the amount of data available across languages.In this paper we address this issue and propose Conception, a novel technique for building language-independent vector representations of concepts which places multilinguality at its core while retaining explicit relationships between concepts.Our approach results in highcoverage representations that outperform the state of the art in multilingual and cross-lingual Semantic Word Similarity and Word Sense Disambiguation, proving particularly robust on lowresource languages.Conception -its software and the complete set of representations -is available at https://github.com
THE USE OF WHATSAPP APP IN DISTANCE LANGUAGE LEARNING IN PANDEMIC COVID-19: A CASE STUDY IN NAHDLATUL ULAMA UNIVERSITY OF YOGYAKARTA

Lately, pandemic covid-19 has changed the educational field including learning methods from conventional learning to distance learning. This shift needed more adaptation for all parts like stakeholders, teachers, and also students. Evidently, various efforts have been made by teachers in simplifying teaching and learning such as applying some digital technologies like WhatsApp App, an asynchronous communication to reach the students’ presence with any spaces and time. This study aimed to describe and to evaluate the use of utilizing WhatsApp App in distance language learning for the English Education Department Students of Nahdlatul Ulama University of Yogyakarta. The research design of this study was a case study. Furthermore, this study gave the detailed information, description, and understanding deeply in relation to the use of WhatsApp App in remote teaching. Students’ perception and the factors affecting the effectiveness were investigated through a questionnaire. The results showed that WhatsApp App can support and helped the language learning to be more effective and efficient for the reason that it was accessible and low technology for distance learning. The students were more responsive and enthusiastic in joining the class. Indeed, it has given more facilities and satisfaction in practice.
Facts2Story: Controlling Text Generation by Key Facts

Recent advancements in self-attention neural network architectures have raised the bar for openended text generation.Yet, while current methods are capable of producing a coherent text which is several hundred words long, attaining control over the content that is being generated-as well as evaluating it-are still open questions.We propose a controlled generation task which is based on expanding a sequence of facts, expressed in natural language, into a longer narrative.We introduce human-based evaluation metrics for this task, as well as a method for deriving a large training dataset.We evaluate three methods on this task, based on fine-tuning pre-trained models.We show that while auto-regressive, unidirectional Language Models such as GPT2 produce better fluency, they struggle to adhere to the requested facts.We propose a plan-andcloze model (using fine-tuned XLNet) which produces competitive fluency while adhering to the requested content.
The Quality Improvement Indicators of the Curriculum at the Technical and Vocational Higher Education

The present study seeks to develop and investigate the quality improvement indicators of the curriculum of vocational higher education in technical and vocational universities.The study employed the mixed methods research design.Two qualitative and quantitative research methods were employed for data collection.In the qualitative section, the population consisted of curriculum experts, faculty members of Iranian technical and vocational universities, and employers.In the quantitative section, the population consisted of all instructors in Shariaty Technical College in Tehran.The research data were collected through semi-structured interviewing technique and a researcher-made questionnaire.The results of qualitative research showed that in order to improve the quality of technical and vocational higher education curricula, solutions such as tailoring curricula to the professional needs of businesses, emphasizing professional standards and competencies, paying attention to focal competencies, link between employers and planners, content modulation, content applicability, work-based teaching, real-time teaching, science and practice integration, group-based and participatory teaching, performance-based assessment, student-based assessment of students' roles and tasks in learning groups, emphasis on practical work and team projects are effective on assessment.the quantitative section results indicate that the indicators obtained in the qualitative phase are valid and endorsed by the participants.
Soft Robotics and Posthuman Entities

Through collaborative media arts practice we explore texture morphing soft robotics as an artistic medium of expression. We present an installation, Homo Viridis, that features a soft robotic interface developed to mediate signals between a vascular plant and a human body. The exposition paper discusses how Homo Viridis stages a situation of hybridity where individual, more-than-human subjectivities are mentally and physically intertwined. In conclusion, the paper reflects on how connecting organisms through soft robotic interfaces can actualize visions of a novel being - a 'posthuman entity'. We argue that such a being might be physically composed of organic and synthetic elements that come together, but that it can also exist as a conceptual persona that may initiate discussions on what humans can become.
Team Oulu at SemEval-2020 Task 12: Multilingual Identification of Offensive Language, Type and Target of Twitter Post Using Translated Datasets

With the proliferation of social media platforms, anonymous discussions together with easy online access, reports on offensive content have caused serious concern to both authorities and research communities.Although there is extensive research in identifying textual offensive language from online content, the dynamic discourse of social media content, as well as the emergence of new forms of offensive language, especially in a multilingual setting, calls for future research in the issue.In this work, we tackled Task A, B, and C of Offensive Language Challenge at SemEval2020.We handled offensive language in five languages: English, Greek, Danish, Arabic, and Turkish.Specifically, we pre-processed all provided datasets and developed an appropriate strategy to handle Tasks (A, B, & C) for identifying the presence/absence, type and the target of offensive language in social media.For this purpose, we used OLID2019, OLID2020 datasets, and generated new datasets, which we made publicly available.We used the provided unsupervised machine learning implementation for automated annotated datasets and the online Google translation tools to create new datasets as well.We discussed the limitations and the success of our machine learning-based approach for all the five different languages.Our results for identifying offensive posts (Task A) yielded satisfactory accuracy of 0.92 for English, 0.81 for Danish, 0.84 for Turkish, 0.85 for Greek, and 0.89 for Arabic.For the type detection (Task B), the results are significantly higher (.87 accuracy) compared to target detection (Task C), which yields .81accuracy.Moreover, after using automated Google translation, the overall efficiency improved by 2% for Greek, Turkish, and Danish.
UPB @ DANKMEMES: Italian Memes Analysis - Employing Visual Models and Graph Convolutional Networks for Meme Identification and Hate Speech Detection

Certain events or political situations determine users from the online environment to express themselves by using different modalities. One of them is represented by Internet memes, which combine text with a representative image to entail a wide range of emotions, from humor to sarcasm and even hate. In this paper, we describe our approach for the DANKMEMES competition from EVALITA 2020 consisting of a multimodal multi-task learning architecture based on two main components. The first one is a Graph Convolutional Network combined with an Italian BERT for text encoding, while the second is varied between different image-based architectures (i.e., ResNet50, ResNet152, and VGG-16) for image representation. Our solution achieves good performance on the first two tasks of the current competition, ranking 3rd for both Task 1 (.8437 macro-F1 score) and Task 2 (.8169 macro-F1 score), while exceeding by high margins the official baselines.