Reviewerly/exHarmony huggingface dataset:
Combined Architectures of Denoising Filter and Local Binary Patterns Feature Extraction

This paper implemented combined architectures of denoising filter and feature extraction, which can be run simultaneously. Median filter and hybrid median filter are used for denoising images. Local binary patterns are used for extracting features. Both operations can be operated on the same cycle using the parallel and pipelined design to improve the performance of the edge computing side. Because the two denoising filters have different operations, the hardware resource utilization of the two combined architectures has been compared on FPGA implementations. The circuit of using hybrid median filter achieves nearly half of the hardware resources of the median filter.
Emotional Dialogue Generation Based on Conditional Variational Autoencoder and Dual Emotion Framework

An excellent dialogue system needs to not only generate rich and diverse logical responses but also meet the needs of users for emotional communication. However, despite much work, these two problems have not been solved. In this paper, we propose a model based on conditional variational autoencoder and dual emotion framework (CVAE-DE) to generate emotional responses. In our model, latent variables of the conditional variational autoencoder are adopted to promote the diversity of conversation. A dual emotion framework is adopted to control the explicit emotion of the response and prevent the conversation from generating emotion drift indicating that the emotion of the response is not related to the input sentence. A multiclass emotion classifier based on the Bidirectional Encoder Representations from Transformers (BERT) model is employed to obtain emotion labels, which promotes the accuracy of emotion recognition and emotion expression. A large number of experiments show that our model not only generates rich and diverse responses but also is emotionally coherent and controllable.
READING DIGITAL TEXT AS A NEW LITERACY IN ELT: TEACHERS' PERCEPTION & PRACTICES

Technological developments in the 21st-century impacted teaching and learning English. one of the impacts of this technological development is reading activities and competencies, reading experienced a phase of change from printed text to digital text. The change from Printed text to digital text requires new literacies, such as locating information and synthesizing online information. This study aims to investigate teachers' perceptions of reading digital texts in ELT. About twenty-three ELT teachers, both in junior and senior high schools around East Java, were involved in this study. An online open questionnaire was distributed to portray ELT teachers' attitudes, knowledge, and practices in reading digital text. The study reveals that all teachers have a positive attitude toward the practice of reading digital text. However, most teachers have misconceptions about knowledge and practice in implementing reading digital text activities. Surprisingly, all teachers in this study claimed that they never received a particular workshop to read digital text activities for English class. Hopefully, this study's result could contribute to the ELT curriculum in responding to the need to facilitate the new literacy in (online) reading and teacher professional development to develop teachers' reading digital text competencies. However, teachers as facilitators in classroom learning activities must recognize new literacy in online reading (especially reading digital text) to help students achieve reading competence in today's digital-based information era.
RELATIONSHIP OF EMPLOYEE TRAINING, EMPLOYEE EMPOWERMENT, TEAM WORK WITH JOB SATISFACTION

Current research is designed to check the impact of employee training, employee empowerment, team work on job satisfaction. Employee training, employee empowerment, team work are independent variables and job satisfaction is dependent variable. Overall, 160 employees of banks employees who are working at managerial level are selected randomly. Results indicates that employee training, employee empowerment, team work has significant positive impact on job satisfaction. It is also seen that teamwork is emerging as strong predictor of job satisfaction the employee empowerment is second good predictor of job satisfaction. In future researches, this kind of researches will be also conducted in other kind of industries like hotel industry, telecom sector and education sector. In future researches some other potential predictors of job satisfaction like employee compensation, employee appraisal will also include. It is also better to include some better outcomes like employee loyalty and employee productivity for generalization.
Investigation on Instagram Android-based using Digital Forensics Research Workshop Framework

The development of social media especially the Instagram app is very rapid.Instagram users usually do activities at any time by using Instagram as a communication tool to share information using an Android smartphone.One of the problems that social media doesn't solve is cybercrime among Instagram users.The case can be examined to obtain digital evidence in the form of chats and pictures/photos on Android smartphones.The investigation in this study uses the Digital Forensics Research Workshop (DFRWS) method which provides several stages of identification, maintenance, collection, examination, analysis, presentation.The process of data acquisition using OXYGEN forensics generates data in the form of photos/images and conversational text on the Instagram messaging service, while the acquisition with JSON Viewer only results in conversational sessions only.From the resulting data will be used as evidence.
UZH at SemEval-2020 Task 3: Combining BERT with WordNet Sense Embeddings to Predict Graded Word Similarity Changes

CoSimLex is a dataset that can be used to evaluate the ability of context-dependent word embeddings for modeling subtle, graded changes of meaning, as perceived by humans during reading.At SemEval-2020, task 3, subtask 1 is about "predicting the (graded) effect of context in word similarity", using CoSimLex to quantify such a change of similarity for a pair of words, from one context to another.Here, a meaning shift is composed of two aspects, a) discrete changes observed between different word senses, and b) more subtle changes of meaning representation that are not captured in those discrete changes.Therefore, this SemEval task was designed to allow the evaluation of systems that can deal with a mix of both situations of semantic shift, as they occur in the human perception of meaning.The described system was developed to improve the BERT baseline provided with the task, by reducing distortions in the BERT semantic space, compared to the human semantic space.To this end, complementarity between 768-and 1024-dimensional BERT embeddings, and average word sense vectors were used.With this system, after some fine-tuning, the baseline performance of 0.705 (uncentered Pearson correlation with human semantic shift data from 27 annotators) was enhanced by more than 6%, to 0.7645.We hope that this work can make a contribution to further our understanding of the semantic vector space of human perception, as it can be modeled with context-dependent word embeddings in natural language processing systems.
MSR India at SemEval-2020 Task 9: Multilingual Models Can Do Code-Mixing Too

In this paper, we present our system for the SemEval 2020 task on code-mixed sentiment analysis.Our system makes use of large transformer based multilingual embeddings like mBERT.Recent work has shown that these models posses the ability to solve code-mixed tasks in addition to their originally demonstrated cross-lingual abilities.We evaluate the stock versions of these models for the sentiment analysis task and also show that their performance can be improved by using unlabelled code-mixed data.Our submission (username Genius1237) achieved the second rank on the English-Hindi subtask with an F1 score of 0.726.
Remote sensing data retouching based on image inpainting algorithms in the forgery generation problem

We investigate image retouching algorithms for generating forgery Earth remote sensing data. We provide an overview of existing neural network solutions in the field of generation and inpainting of remote sensing images. To retouch Earth remote sensing data, we use image-inpainting algorithms based on convolutional neural networks and generative-adversarial neural networks. We pay special attention to a generative neural network with a separate contour prediction block that includes two series-connected generative-adversarial subnets. The first subnet inpaints contours of the image within the retouched area. The second subnet uses the inpainted contours to generate the resulting retouch area. As a basis for comparison, we use exemplar-based algorithms of image inpainting. We carry out computational experiments to study the effectiveness of these algorithms when retouching natural data of remote sensing of various types. We perform a comparative analysis of the quality of the algorithms considered, depending on the type, shape and size of the retouched objects and areas. We give qualitative and quantitative characteristics of the efficiency of the studied image inpainting algorithms when retouching Earth remote sensing data. We experimentally prove the advantage of generative-competitive neural networks in the construction of forgery remote sensing data.
Digitalization of Traditional Classrooms: A Students’ Perspective

ABSTRACT Covid-19 is fiercely hurting the field of academics and education. Millions of learners and teachers and educational institutions have been adversely affected. Educational institutions have scrapped traditional physical classroom teaching during these challenging times and have adopted e-learning models. However, there exist many discrepancies in the process of digitalization of classrooms. Therefore, this present study aims to evaluate the ramifications of the Covid-19 pandemic on the education sector and provide a student-oriented perspective to address the challenges in this process. The study is based on an independent online survey conducted by authors in which 212 undergraduate students of one of India’s prestigious universities, the University of Delhi, have participated. The survey unveiled that many colleges are arranging online classes for students via various technological platforms available such as Google Meet, Zoom, MS Teams, Skype, etc. However, it has been observed that almost half of the students are attending classes irregularly and do not possess requisite study material for self-study. The survey reports that many students face depression and anxiety-related problems during the lockdown period. Other issues include improper internet connectivity and the non-availability of the devices. About three-fourth of students have expressed their concerns that education may become a costly affair for them as their family income dwindles. The study stresses developing more innovative and interactive teaching methods, as online teaching is monotonous and dull. Covid-19 has led to a decline in the students’ family income, making education costly to many students. More innovative teaching practices for better learning experiences and adequate funding of the educational institutes for infrastructural development is the need of the hour.
YNU-HPCC at SemEval-2020 Task 11: LSTM Network for Detection of Propaganda Techniques in News Articles

This paper summarizes our studies on propaganda detection techniques for news articles in the SemEval-2020 task 11.This task is divided into the SI and TC subtasks.We implemented the GloVe word representation, the BERT pretraining model, and the LSTM model architecture to accomplish this task.Our approach achieved good results for both the SI and TC subtasks.The macro-F 1 -score for the SI subtask is 0.406, and the micro-F 1 -score for the TC subtask is 0.505.Our method significantly outperforms the officially released baseline method, and the SI and TC subtasks rank 17th and 22nd, respectively, for the test set.This paper also compares the performances of different deep learning model architectures, such as the Bi-LSTM, LSTM, BERT, and XGBoost models, on the detection of news promotion techniques.
Building Large-Scale English and Korean Datasets for Aspect-Level Sentiment Analysis in Automotive Domain

We release large-scale datasets of users' comments in two languages, English and Korean, for aspect-level sentiment analysis in automotive domain.The datasets consist of 58,000+ commentaspect pairs, which are the largest compared to existing datasets.In addition, this work covers new language (i.e., Korean) along with English for aspect-level sentiment analysis.We build the datasets from automotive domain to enable users (e.g., marketers in automotive companies) to analyze the voice of customers on automobiles.We also provide baseline performances for future work by evaluating recent models on the released datasets.
Identifying Annotator Bias: A new IRT-based method for bias identification

A basic step in any annotation effort is the measurement of the Inter Annotator Agreement (IAA).An important factor that can affect the IAA is the presence of annotator bias.In this paper we introduce a new interpretation and application of the Item Response Theory (IRT) to detect annotators' bias.Our interpretation of IRT offers an original bias identification method that can be used to compare annotators' bias and characterise annotation disagreement.Our method can be used to spot outlier annotators, improve annotation guidelines and provide a better picture of the annotation reliability.Additionally, because scales for IAA interpretation are not generally agreed upon, our bias identification method is valuable as a complement to the IAA value which can help with understanding the annotation disagreement.
SPATIAL MODELING OF THE THREAT OF DAMAGE TO THE PEATLAND ECOSYSTEM IN THE MAINLAND OF BENGKALIS REGENCY, RIAU PROVINCE

Peatlands are the stretch of ecosystem landscape with unique characteristics, both physically, chemically, and biodiversity. Anthropogenic activities in peatland use and disasters pose a threat to the preservation of the peatland ecosystem, which has impacts toward abiotic to the element of biodiversity (biotic). The purpose of this research is to model how the threat of the peatland ecosystem by using spatial data modeling. The method in this research using cloud-based GIS data analysis from Google earth engine, modeling distance parameter to variable modeling of interaction among landscapes on the peatland, and weight sum the value over raster-based spatial layer to determinate the thereat in the peatland ecosystem. The results of this study found zones where hot spots often occur. Modeling with euclidean distance to all modeling variables (except temperature) gives a clear effect on how the threats from each landscape interact with each other. We found that the threat of peatland damage in the high threat class dominates the plantation area reaching 30.9% of the total peatland area, whereas the forest landscape only has a high threat with a percentage of 1.9% and a low threat which the ecosystem is stable and natural reaching over 34.7 %. From this model, we succeeded in bringing up the idea to determine the priority area for policies where need to be done in handling the protection of peatland ecosystems, especially in plantations where the highest percentage of the ecosystem threat is in the high level with integrated peatland management.&#x0D; Keywords: Peatland ecosystem, landscape, threat
TUE at SemEval-2020 Task 1: Detecting Semantic Change by Clustering Contextual Word Embeddings

This paper describes our system for SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection.Target words of corpora from two different time periods are classified according to their semantic change.The languages covered are English, German, Latin, and Swedish.Our approach involves clustering ELMo embeddings using DBSCAN and K-means.For a more fine grained detection of semantic change we take the Jensen-Shannon Distance metric and rank the target words from strongest to weakest change.The results show that this is a valid approach for the classification subtask where we rank 11th out of 21 groups with an accuracy score of 61.2%.For the ranking subtask we score a Spearman's rank-order correlation coefficient of 0.087 which places us on rank 19.
Does the Commercial Bank's Loans Affect Economic Growth? Empirical Evidence for the Real Sector Economy in Kosovo (2005-2018)

The study aims to analyze the impact of credit policies of commercial banks on financing and development of the real sector of Kosovo's economy. In this context, some statistical and econometric models and techniques have been applied in order to test the impact of commercial banks through the lending process in the development of the real sector of Kosovo's economy for the period 2005-2018, using time series on a monthly basis. The empirical results of this study prove that commercial banks through the lending process have had a positive substantive impact on the development of the real sector in the economy of Kosovo. Economic development cannot happen without the development of the private sector and banks are the ones who can help and are helping in this regard. This study will provide a theoretical and practical analysis of contemporary forms of real sector lending, as well as, the importance of credit policy reform in financing and developing this sector and provide empirical evidence of how much bank loans have affected in the development of the real sector of Kosovo's economy.
Students’ perception of online communication language learning through Instagram

The use of Social Media has become pervasive. As an educator, this might facilitate the learning experiences, especially for second language learning. Bring that issues, this study intended to delve into students' perception regarding their experience in using mobile-assisted language learning (MALL) tools especially Instagram for speaking activity in English for Specific Purposes class. Besides, this study also explored students' opinions regarding the use of Instagram for language learning purposes. Two sets of questionnaires were distributed and analyzed. The result indicated that the participants expressed a positive perception of the use of Instagram for learning language purposes. The finding revealed that, among the participants, Instagram is the most popular social media. The positive students’ perception is that learning through Instagram is effective because it provides a new way of learning language and it accelerates interactive vocabulary enrichment. DOI: 10.26905/enjourme.v5i2.5202
Order-Revealing Encryption Scheme with Comparison Token for Cloud Computing

Order-preserving encryption (OPE) is a basic paradigm for the outsourced database where the order of plaintexts is kept in ciphertexts. OPE enables efficient order comparison execution while providing privacy protection. Unfortunately, almost all the previous OPE schemes either require numerous rounds of interactions or reveal more information about the encrypted database (e.g., the most significant bit). Order-revealing encryption (ORE) as a generalization is an encryption scheme where the order of plaintexts can be evaluated by running a comparison algorithm. Therefore, it is desirable to design an efficient ORE scheme which addresses above efficiency and security issues. In this paper, we propose a noninteractive ORE scheme from prefix encoding and Bloom filter techniques. The proposed scheme is an encryption scheme where a cloud service provider cannot evaluate the order of plaintexts until a comparison token is provided. The security analysis illustrates that our scheme achieves ideal security with frequency hiding. Furthermore, we illustrate a secure range query scheme through designing an encrypted tree structure named PORE tree from the above ORE scheme. The PORE tree reveals the order between different nodes and leaves encrypted data items in the same node incomparable even after query execution. Finally, the experimental evaluation shows the high efficiency of the proposed ORE scheme and range query scheme.
RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion

Many graph embedding approaches have been proposed for knowledge graph completion via link prediction.Among those, translating embedding approaches enjoy the advantages of lightweight structure, high efficiency and great interpretability.Especially when extended to complex vector space, they show the capability in handling various relation patterns including symmetry, antisymmetry, inversion and composition.However, previous translating embedding approaches defined in complex vector space suffer from two main issues: 1) representing and modeling capacities of the model are limited by the translation function with rigorous multiplication of two complex numbers; and 2) embedding ambiguity caused by one-to-many relations is not explicitly alleviated.In this paper, we propose a relation-adaptive translation function built upon a novel weighted product in complex space, where the weights are learnable, relation-specific and independent to embedding size.The translation function only requires eight more scalar parameters each relation, but improves expressive power and alleviates embedding ambiguity problem.Based on the function, we then present our Relation-adaptive translating Embedding (RatE) approach to score each graph triple.Moreover, a novel negative sampling method is proposed to utilize both prior knowledge and self-adversarial learning for effective optimization.Experiments verify RatE achieves state-of-the-art performance on four link prediction benchmarks.
UoB at SemEval-2020 Task 1: Automatic Identification of Novel Word Senses

Much as the social landscape in which languages are spoken shifts, language too evolves to suit the needs of its users.Lexical semantic change analysis is a burgeoning field of semantic analysis which aims to trace changes in the meanings of words over time.This paper presents an approach to lexical semantic change detection based on Bayesian word sense induction suitable for novel word sense identification.This approach is used for a submission to SemEval-2020 Task 1, which shows the approach to be capable of the SemEval task.The same approach is also applied to a corpus gleaned from 15 years of Twitter data, the results of which are then used to identify words which may be instances of slang. Related WorkApproaches to lexical semantic change detection collect co-occurrence information on words, represented in most approaches by pointwise mutual information scores (Tahmasebi et al., 2018).This information may be used as the basis of an approach, as is used by Sagi et al. (2009) and Gulordava and Baroni (2011) to identify semantic change over time by using context vectors.
PRHLT-UPV at SemEval-2020 Task 12: BERT for Multilingual Offensive Language Detection

The present paper describes the system submitted by the PRHLT-UPV team for the task 12 of SemEval-2020: OffensEval 2020.The official title of the task is Multilingual Offensive Language Identification in Social Media, and aims to identify offensive language in texts.The languages included in the task are English, Arabic, Danish, Greek and Turkish.We propose a model based on the BERT architecture for the analysis of texts in English.The approach leverages knowledge within a pre-trained model and performs fine-tuning for the particular task.In the analysis of the other languages the Multilingual BERT is used, which has been pre-trained for a large number of languages.In the experiments, the proposed method for English texts is compared with other approaches to analyze the relevance of the architecture used.Furthermore, simple models for the other languages are evaluated to compare them with the proposed one.The experimental results show that the model based on BERT outperforms other approaches.The main contribution of this work lies in this study, despite not obtaining the first positions in most cases of the competition ranking.