Reviewerly/exHarmony huggingface dataset:
A SURVEY ON SARCASM DETECTION APPROACHES

Natural Language Processing (NLP) is always one of the interesting topics among researchers.To understand the perfect meaning of what depicted in the conversation is always a helping factor to solve different tasks and enhance the accuracy of different applications.Sentiment analysis uses the NLP techniques and learning models like machine learning and deep learning algorithms to understand sentiments expressed in the given data.Sentiment analysis is an approach to find the contextual meaning expressed in the textual data.Sarcasm detection comes as part of sentiment analysis because sarcasm is a kind of sentiment where individuals convey their feelings about a particular topic indirectly.People means the entire opposite of the surface content of the sentence.This unique characteristic of the sarcastic sentence makes it difficult to plot sarcasm.This paper will discuss the works done in the area of sarcasm detection, different techniques and challenges in sarcasm detection.
Inverse Multiscale Discrete Radon Transform by Filtered Backprojection

The Radon transform is a valuable tool in inverse problems such as the ones present in electromagnetic imaging. Up to now the inversion of the multiscale discrete Radon transform has been only possible by iterative numerical methods while the continuous Radon transform is usually tackled with the filtered backprojection approach. In this study, we will show, for the first time, that the multiscale discrete version of Radon transform can as well be inverted with filtered backprojection, and by doing so, we will achieve the fastest implementation until now of bidimensional discrete Radon inversion. Moreover, the proposed method allows the sacrifice of accuracy for further acceleration. It is a well-conditioned inversion that exhibits a resistance against noise similar to that of iterative methods.
Inconsistencies in Crowdsourced Slot-Filling Annotations: A Typology and Identification Methods

Slot-filling models in task-driven dialog systems rely on carefully annotated training data.However, annotations by crowd workers are often inconsistent or contain errors.Simple solutions like manually checking annotations or having multiple workers label each sample are expensive and waste effort on samples that are correct.If we can identify inconsistencies, we can focus effort where it is needed.Toward this end, we define six inconsistency types in slot-filling annotations.Using three new noisy crowd-annotated datasets, we show that a wide range of inconsistencies occur and can impact system performance if not addressed.We then introduce automatic methods of identifying inconsistencies.Experiments on our new datasets show that these methods effectively reveal inconsistencies in data, though there is further scope for improvement.
A Critical Discourse Analysis of Gaza Marches of Return Coverage in Selected Newspapers

The current study aimed at discovering biases through comparing the used journalistic and lexicalizations practices in quotations patterns and representations of social actors in news coverage of the Gaza Marches of Return. The selected newspapers were The Guardian, the New York Times, The Jordan Times and Haaretz. The study sample comprised 32 news articles and 8 editorials. The findings of the study revealed that more space was given to Israeli political and military actors over their Palestinian counterparts in the New York Times and Haaretz. The Guardian and the Jordan Times quotations of Palestinian civilian actors focused on human suffering and casualties themes while Haaretz and the NYT quoted them instigating deadly attacks among other themes. The use of negative themes along with negative speakers’ descriptions of Palestinian political actors revealed biased stances against Palestinian Civilian Actors. In addition, the used reporting verbs, unbalanced quotations distributions and word counts of social actors’ quotations pointed at biased practices and adoption of one party narrative in the case of the NYT, Haaretz and the Jordan Times.
BMEAUT at SemEval-2020 Task 2: Lexical Entailment with Semantic Graphs

In this paper we present a novel rule-based, language independent method for determining lexical entailment relations using semantic representations built from Wiktionary definitions.Combined with a simple WordNet-based method our system achieves top scores on the English and Italian datasets of the Semeval-2020 task "Predicting Multilingual and Cross-lingual (graded) Lexical Entailment" (Glavaš et al., 2020).A detailed error analysis of our output uncovers future directions for improving both the semantic parsing method and the inference process on semantic graphs.reported in this document.
Does Chinese BERT Encode Word Structure?

Contextualized representations give significantly improved results for a wide range of NLP tasks.Much work has been dedicated to analyzing the features captured by representative models such as BERT.Existing work finds that syntactic, semantic and word sense knowledge are encoded in BERT.However, little work has investigated word features for character-based languages such as Chinese.We investigate Chinese BERT using both attention weight distribution statistics and probing tasks, finding that (1) word information is captured by BERT; (2) word-level features are mostly in the middle representation layers; (3) downstream tasks make different use of word features in BERT, with POS tagging and chunking relying the most on word features, and natural language inference relying the least on such features.
A Unified Framework for Multilingual and Code-Mixed Visual Question Answering

In this paper, we propose an effective deep learning framework for multilingual and code- mixed visual question answering. The pro- posed model is capable of predicting answers from the questions in Hindi, English or Code- mixed (Hinglish: Hindi-English) languages. The majority of the existing techniques on Vi- sual Question Answering (VQA) focus on En- glish questions only. However, many applica- tions such as medical imaging, tourism, visual assistants require a multilinguality-enabled module for their widespread usages. As there is no available dataset in English-Hindi VQA, we firstly create Hindi and Code-mixed VQA datasets by exploiting the linguistic properties of these languages. We propose a robust tech- nique capable of handling the multilingual and code-mixed question to provide the answer against the visual information (image). To better encode the multilingual and code-mixed questions, we introduce a hierarchy of shared layers. We control the behaviour of these shared layers by an attention-based soft layer sharing mechanism, which learns how shared layers are applied in different ways for the dif- ferent languages of the question. Further, our model uses bi-linear attention with a residual connection to fuse the language and image fea- tures. We perform extensive evaluation and ablation studies for English, Hindi and Code- mixed VQA. The evaluation shows that the proposed multilingual model achieves state-of- the-art performance in all these settings.
USARP method

Usability is considered one of the main attributes of software quality. Thus, it becomes necessary to deal with usability requirements at early stages of software development. The cost of solving usability problems at these stages is lower, as it requires less time and effort from the development team. However, there is still a need for solutions that can support the inclusion of usability at early stages of development. In this context, this paper presents USARP, a method to elicit and describe USAbility Requirements with Personas and user stories. The proposed method was created by adopting Design Science Research. USARP proposes the joint use of: (i) a technique for creating personas, (ii) user stories for specifying requirements and (iii) guidelines for eliciting functional usability aspects for enriching the user stories. A feasibility study with novice software engineers was performed to validate the effectiveness and the suitability of the procedures proposed in the method. The results showed that the method is effective in obtaining the usability requirements. In addition, some improvements in its use procedures were identified. A new version of the method was developed based on the results.
THE SEARCH FOR DETERMINANTS OF KOSOVAN SMALL AND MEDIUM ENTERPRISES PERFORMANCE

The main purpose of this study is to identify determinants of SMEs performance, followed by the impact of Information Technology on SMEs profitability.By reviewing the existing literature and using the secondary data, this paper empirically investigates the determinants of SMEs growth in Kosovo; the fundamental obstacles faced by SMEs in Kosovo; the impact of Information Technology on SMEs performance and, it proposes strategies and measures to maximise Information Technology adoption from Kosovan SMEs.For the research purpose in this paper are used the secondary data gathered from Business Support Center Kosovo, who developed a survey from 500 SMEs in Kosovo.The combination of the variables related to entrepreneur, firm, business environment and IT adoption in a logit regression model suggest that entrepreneurs age, business age, introduction of any new method of marketing other than existing in the market for products/services during last three years from the firm are significant, whereas Internet use from SMEs resulted the most significant variable for SMEs profitability.
PREDICTING THE PRIORITY OF BUG REPORTS USING CLASSIFICATION ALGORITHMS

Severity of the bug reports are assigned by the user, developer and tester of the software, but priority is assigned by the developers.The developer assigns different priorities such as P1, P2, P3, P4, and P5 where P1 is higher priority and P5 is lower priority.If a bug report contains the priority level P1, then it will be given higher priority for fixing.In order to prioritize which bug to be fixed first based on the priority, priority information is required.Even though, the priority is assigned by developer, sometimes it may incorrect, because of busy schedule or inexperienced developer.That time, developer can use this recommendation system for more accurate priority assignment and also time may be saved.In this work, predicting the priority of bug report is presented using different classification algorithms such as Naïve bayes, Simple Logistic and Random Tree.Among the three classifiers Simple Logistics gives better result over other two classifiers.
A Visual Servoing Method based on Point Cloud

A visual servoing method based on point cloud is proposed in this paper. This method does not need any image data and simply relies on dense point cloud information, thus is independent of illumination changes. Meanwhile, this method is free of feature matching which is widely employed in traditional visual servoing methods. Experiments on a real robot prove that this proposed method is effective and robust to regular scene as well as the scene in which the object is scarce in texture features.
Exploring Distribution Rules and Variation Trends of Precipitation in the Upper Lijiang River from 1951 to 2016, Guangxi Province, China

Wang, H.; Yan, W.; Wang, J., and Duan, W., 2020. Exploring distribution rules and variation trends of precipitation in the upper Lijiang River from 1951 to 2016, Guangxi Province, China. In: Hu, C. and Cai, M. (eds.), Geo-informatics and Oceanography. Journal of Coastal Research, Special Issue No. 105, pp. 1–5. Coconut Creek (Florida), ISSN 0749-0208.Based on the relevant modules of ArcGIS 10.5 platform, the singly independent precipitation datum of 25 hydrological and meteorological observation stations from 1960 to 2016 was converted into areal datum of different statistical units through multiscale transformation to study change rules and trends of precipitation in the upper Lijiang River. The variation coefficient and Mann-Kendall test methods were applied to analyzing the difference and significance of precipitation. According to the study, precipitation distribution within a year was highly uneven with an obvious difference between the rainy and dry seasons; the interannual changes of precipitation showed a slightly increasing trend in the high and low interlaced. The increasing trends of precipitation during the rainy season and decreasing during the dry season will further aggravate the contradiction between water resource shortages during the dry season and increasing flooding disasters during the rainy season in the Lijiang River basin.
A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction

Unsupervised Bilingual Dictionary Induction methods based on the initialization and the selflearning have achieved great success in similar language pairs, e.g., English-Spanish.But they still fail and have an accuracy of 0% in many distant language pairs, e.g., English-Japanese.In this work, we show that this failure results from the gap between the actual initialization performance and the minimum initialization performance for the self-learning to succeed.We propose Iterative Dimension Reduction to bridge this gap.Our experiments show that this simple method does not hamper the performance of similar language pairs and achieves an accuracy of 13.64∼55.53%between English and four distant languages, i.e., Chinese, Japanese, Vietnamese and Thai.
Creation of Corpus and analysis in Code-Mixed Kannada-English Twitter data for Emotion Prediction

Emotion prediction is a critical task in the field of Natural Language Processing (NLP).There has been a significant amount of work done in emotion prediction for resource-rich languages.There has been work done on code-mixed social media corpus but not on emotion prediction of Kannada-English code-mixed Twitter data.In this paper, we analyze the problem of emotion prediction on corpus obtained from code-mixed Kannada-English extracted from Twitter annotated with their respective 'Emotion' for each tweet.We experimented with machine learning prediction models using features like Character N-Grams, Word N-Grams, Repetitive characters, and others on SVM and LSTM on our corpus, which resulted in an accuracy of 30% and 32%, respectively.
Image Dehazing Method Based on Multi-scale Convolutional Neural Network and Classification Statistics

Abstract：Traditional methods of image dehazing can distort color in areas such as the sky, white clouds, and bright areas.To address these problems, a three-step method is proposed for removing image hazing using a multi-scale convolutional neural network (MCNN) and classification statistics.First, the MCNN is used to estimate the transmittance of the image.Second, the estimated transmittance is classified and the pixel values of the sky, white clouds, and other bright regions in the dark channel are determined.Finally, the radiance of the scene is smoothed by a low-pass Gaussian filter to produce a restored haze-free image.Experimental results show that this method preserves the color in bright areas after the image is defogged, retaining the natural appearance of the image.The proposed method achieves improved dehazing on both synthetic and real images.
Bingo Games in students’ vocabulary and reading comprehension

The aim of the present study is to explore the effect of using Bingo Games on students’ vocabulary and reading comprehension. The subjects of the study were tenth-grade students of SMAN 8 Mataram. The researchers used experimental design by applying random sampling technique. The researchers used two group classes, namely X MIA 1 as experimental and X MIA II as control class. In collecting the data, vocabulary and reading test were used as an instrument. The data was analyzed used descriptive and inferential statistics. The result revealed that there were significant improvement on the students’ posttest. It proves that Bingo games was one of alternative strategy that can be applying in teaching vocabulary and reading comprehension. DOI : 10.26905/enjourme.v 5 i2. 5056
A Graph-Based Approach to Automatic Convolutional Neural Network Construction for Image Classification

Convolutional neural networks (CNNs) have achieved great success in the image classification field in recent years. Usually, human experts are needed to design the architectures of CNNs for different tasks. Evolutionary neural network architecture search could find optimal CNN architectures automatically. However, the previous representations of CNN architectures with evolutionary algorithms have many restrictions. In this paper, we propose a new flexible representation based on the directed acyclic graph to encode CNN architectures, to develop a genetic algorithm (GA) based evolutionary neural network architecture, where the depth of candidate CNNs could be variable. Furthermore, we design new crossover and mutation operators, which can be performed on individuals of different lengths. The proposed algorithm is evaluated on five widely used datasets. The experimental results show that the proposed algorithm achieves very competitive performance against its peer competitors in terms of the classification accuracy and number of parameters.
Graph Attention Network with Memory Fusion for Aspect-level Sentiment Analysis

Aspect-level sentiment analysis(ASC) predicts each specific aspect term’s sentiment polarity in a given text or review. Recent studies used attention-based methods that can effectively improve the performance of aspect-level sentiment analysis. These methods ignored the syntactic relationship between the aspect and its corresponding context words, leading the model to focus on syntactically unrelated words mistakenly. One proposed solution, the graph convolutional network (GCN), cannot completely avoid the problem. While it does incorporate useful information about syntax, it assigns equal weight to all the edges between connected words. It may still incorrectly associate unrelated words to the target aspect through the iterations of graph convolutional propagation. In this study, a graph attention network with memory fusion is proposed to extend GCN’s idea by assigning different weights to edges. Syntactic constraints can be imposed to block the graph convolutional propagation of unrelated words. A convolutional layer and a memory fusion were applied to learn and exploit multiword relations and draw different weights of words to improve performance further. Experimental results on five datasets show that the proposed method yields better performance than existing methods.
BUT-FIT at SemEval-2020 Task 4: Multilingual Commonsense

This paper describes work of the BUT-FIT's team at SemEval 2020 Task 4 -Commonsense Validation and Explanation.We participated in all three subtasks.In subtasks A and B, our submissions are based on pretrained language representation models (namely ALBERT) and data augmentation.We experimented with solving the task for another language, Czech, by means of multilingual models and machine translated dataset, or translated model inputs.We show that with a strong machine translation system, our system can be used in another language with a small accuracy loss.In subtask C, our submission, which is based on pretrained sequence-to-sequence model (BART), ranked 1st in BLEU score ranking, however, we show that the correlation between BLEU and human evaluation, in which our submission ended up 4th, is low.We analyse the metrics used in the evaluation and we propose an additional score based on model from subtask B, which correlates well with our manual ranking, as well as reranking method based on the same principle.We performed an error and dataset analysis for all subtasks and we present our findings.
An Authentication Scheme Based on Novel Construction of Hash Chains for Smart Mobile Devices

With the increasing number of smart mobile devices, applications based on mobile network take an indispensable role in the Internet of Things. Due to the limited computing power and restricted storage capacity of mobile devices, it is very necessary to design a secure and lightweight authentication scheme for mobile devices. As a lightweight cryptographic primitive, the hash chain is widely used in various cryptographic protocols and one-time password systems. However, most of the existing research work focuses on solving its inherent limitations and deficiencies, while ignoring its security issues. We propose a novel construction of hash chain that consists of multiple different hash functions of different output lengths and employ it in a time-based one-time password (TOTP) system for mobile device authentication. The security foundation of our construction is that the order of the hash functions is confidential and the security analysis demonstrates that it is more secure than other constructions. Moreover, we discuss the degeneration of our construction and implement the scheme in a mobile device. The simulation experiments show that the attacker cannot increase the probability of guessing the order by eavesdropping on the invalid passwords.
DH-FBK @ HaSpeeDe2: Italian Hate Speech Detection via Self-Training and Oversampling

English.Despite the number of approaches recently proposed in NLP for detecting abusive language on social networks, the issue of developing hate speech detection systems that are robust across different platforms is still an unsolved problem.In this paper we perform a comparative evaluation on datasets for hate speech detection in Italian, extracted from four different social media platforms, i.e.Facebook, Twitter, Instagram and What-sApp.We show that combining such platform-dependent datasets to take advantage of training data developed for other platforms is beneficial, although their impact varies depending on the social network under consideration.
Conceptual Model of Flexible Library

The article is devoted to the substantiation of the model of flexible library as a theoretical reference point for the development of libraries in the transition to the information society.The application of the system method, as well as methods of design, modeling, and forecasting based on the analysis of existing theoretical achievements in the field of library science and practical experience of library activity made it possible to propose a model of a flexible library as an open system, which includes such elements as “library space”, “library service”, “human resources” and “client environment”, which are considered taking into account the key characteristics of the information society – networking, service economy, personalization, cooperation, virtualization, involvement. “Library space” is defined as a space in which there is a direct and indirect (through the created products and resources) communication of the librarian and the user both in real and in the virtual environment, both in the library and outside it, which determines the flexibility of the library space. “Library service” is considered as the interaction of the user with the library, which is built around library resources and products. Changes in the range of library products and services determine the flexibility of the library service. “Human resources” is defined as a result of the synergy of the users’ and libraries’ intellectual potential. Its flexibility is achieved through the development of competencies of librarians, structural changes in the organization of library staff, as well as the involvement of subjects of the client environment as partners of the library. Changing the status of some subjects of the client environment from library users to library partners, in turn, affects the flexibility of the “client environment”.Organizational, personnel, service flexibility of the library in the future will ensure its adaptability to the conditions of dynamic changes and the ability to meet the key principles of the information society – ubiquity, timelessness, identification (personalization) and involvement.