Reviewerly/exHarmony huggingface dataset:
Humans in the Loop: Learning to Trust in AI but to What Extent?

Spontaneity makes life worth living. If we had all the answers to every question and could accurately predict the result of our actions and choices before embarking on a particular path into the future, human life would be substantially different—some might even say boring. The fact that we must strive through things ourselves, make mistakes, and learn from them, provides a certain level of freedom that none of us should take for granted. It was not that long ago that we would get into a car with a street directory on our laps, excited to explore a new route for the first time, instead of just delegating our route selection to the shortest path algorithm and relying on it to get us to our destination. While we are all grateful for the ease with which we can now navigate the world, there is an opportunity cost generated by an augmentation–automation paradox.
NLP_Passau at SemEval-2020 Task 12: Multilingual Neural Network for Offensive Language Detection in English, Danish and Turkish

This paper describes a neural network (NN) model that was used for participating in the OffensEval, Task 12 of the SemEval 2020 workshop.The aim of this task is to identify offensive speech in social media, specifically in tweets.The model we used, C-BiGRU, is composed of a Convolutional Neural Network (CNN) along with a bidirectional Recurrent Neural Network (RNN).A multidimensional numerical representation (embedding) for each of the words in the tweets, that were used by the model, was determined using fastText.This was utilized with a dataset of labeled tweets to train the model on detecting combinations of words that may convey an offensive meaning.The model was then used in the sub-task A of the English, Turkish and Danish competitions of the workshop, achieving F1 scores of 90.88%, 76.76% and 76.70%, respectively.
KAFK at SemEval-2020 Task 8: Extracting Features from Pre-trained Neural Networks to Classify Internet Memes

This paper presents two approaches for the internet meme classification challenge of SemEval-2020 Task 8 by Team KAFK (cosec).The first approach uses both text and image features, while the second approach uses only the images.Error analysis of the two approaches shows that using only the images is more robust to the noise in the text on the memes.We utilize pre-trained DistilBERT and EfficientNet to extract features from the text and image of the memes respectively.Our classification systems obtained macro f1 score of 0.3286 for Task A and 0.5005 for Task B.
A Deep Metric Learning Method for Biomedical Passage Retrieval

Passage retrieval is the task of identifying text snippets that are valid answers for a natural language posed question. One way to address this problem is to look at it as a metric learning problem, where we want to induce a metric between questions and passages that assign smaller distances to more relevant passages. In this work, we present a novel method for passage retrieval that learns a metric for questions and passages based on their internal semantic interactions. The method uses a similar approach to that of triplet networks, where the training samples are composed of one anchor (the question) and two positive and negative samples (passages). However,and in contrast with triplet networks, the proposed method uses a novel deep architecture that better exploits the particularities of text and takes into consideration complementary relatedness measures. Besides, the paper presents a sampling strategy that selects both easy and hard negative samples which improves the accuracy of the trained model. The method is particularly well suited for domain-specific passage retrieval where it is very important to take into account different sources of information. The proposed approach was evaluated in a biomedical passage retrieval task, the BioASQ challenge, outperforming standard triplet loss substantially by 10%,and state-of-the-art performance by 26%.
Sonal.kumari at SemEval-2020 Task 12: Social Media Multilingual Offensive Text Identification and Categorization Using Neural Network Models

In this paper, we present our approaches and results for SemEval-2020 Task 12, Multilingual Offensive Language Identification in Social Media (OffensEval 2020).The OffensEval 2020 had three subtasks: A) Identifying the tweets to be offensive (OFF) or non-offensive (NOT) for Arabic, Danish, English, Greek, and Turkish languages, B) Detecting if the offensive tweet is targeted (TIN) or untargeted (UNT) for the English language, and C) Categorizing the offensive targeted tweets into three classes, namely: individual (IND), Group (GRP), or Other (OTH) for the English language.We participate in all the subtasks A, B, and C. In our solution, first we use the pre-trained BERT model for all subtasks, A, B, and C and then we apply the BiLSTM model with attention mechanism (Attn-BiLSTM) for the same.Our result demonstrates that the pretrained model is not giving good results for all types of languages and is compute and memory intensive whereas the Attn-BiLSTM model is fast and gives good accuracy with fewer resources.The Attn-BiLSTM model is giving better accuracy for Arabic and Greek where the pretrained model is not able to capture the complete context of these languages due to lower vocabsize.
Lifting low-gonal curves for use in Tuitman’s algorithm

Consider a smooth projective curve $\overline{C}$ over a finite field $\mathbb{F}_q$, equipped with a simply branched morphism $\overline{C} \to \mathbb{P}^1$ of degree $d \leq 5$. Assume char$\, \mathbb{F}_q > 2$ if $d \leq 4$, and char$\, \mathbb{F}_q > 3$ if $d=5$. In this paper we describe how to efficiently compute a lift of $\overline{C}$ to characteristic zero, such that it can be fed as input to Tuitman's algorithm for computing the Hasse-Weil zeta function of $\overline{C} / \mathbb{F}_q$. Our method relies on the parametrizations of low rank rings due to Delone-Faddeev and Bhargava.
Medication Adherence to Successful Tuberculosis Treatment Outcome among TB/HIV Patient at Prof. Dr. Sulianti Saroso Infectious Disease Hospital

Tuberculosis/Human Immunodeficiency Virus (TB/HIV) co-infection has poorer treatment outcome compared to non-co-infected patients. To benefit from therapy and to avoid contracting treatment-resistant strains, the individuals must adherent to medications. There is limited information regarding successful TB treatment outcomes and their associated factors. Thus, the study was designed to identify medication adherence associated with treatment outcomes among TB/HIV Patients at Prof. Dr. Sulianti Saroso Infectious Disease Hospital. This research was an observational study with retrospective approach from January 2015 to December 2017 by taking data from medical records and TB-01 form of TB/HIV patients in the outpatient clinic of the disease which would be used as a sample. Regarding compliance in treatment with first visit for 2-4 months, an approach using secondary data which were TB-01 Form, as well as HIV Care and Antiretroviral Therapy summary forms was used. The statistical analysis used was the bivariate analysis with the chi-square test as the statistical test. A total of 114 patients were included in the study. The outcome of treatment success obtained from this study had a patient cure rate of 91.30%. There is a significant relationship between medication adherence to successful tuberculosis treatment outcome with p-value= 0.012; OR= 5.684. There is a meaningful relation between medication compliance with treatment outcome. It is when on medication adherence, the treatment results can be declared cured in accordance with predetermined criteria, while when not on medication adherence, the treatment results are not declared cured.
The Impact of Online Classes on Adolescent's Internet Use

After switching to online classes due to COVID-19, the biggest concern is that teenagers are leading to excessive Internet immersion and addiction.This study, targeting male middle and high school students (N=940), attempted to find out the current status of Internet use for learning and non-learning purposes, the impact and satisfaction of online classes on daily life, and differences according to amount of online class, GPA, devices used, and age.As a result, after switching to online classes, students with a large amount of online classes evaluated themselves to be better in terms of learning effects, regular lifestyle habits, and personal time use.Online classes did not necessarily increase Internet usage for non-learning purposes.But, GPA was an important variable, and students with low GPA had fewer online classes, and increased internet usage and internet addiction scores after conversion to online classes.In addition, when middle school students used phones, Internet usage for non-learning purposes was higher, and amount of increase in Internet use and the degree of Internet addiction were also higher after switching to online class.The implications related to these results were discussed.
KDELAB at SemEval-2020 Task 12: A System for Estimating Aggression of Tweets Using Two Layers of BERT Features

In recent years, with the development of social network services and video distribution services, there has been a sharp increase in offensive posts.In this paper, we present our approach for detecting hate speech in tweets defined in the SemEval-2020 Task 12. Specifically, we describe our ensemble system, based on "BERT Large", having two parallel neural net component models, where one component model consists of MLP with input taken from the 23rd layer output from BERT, while another component model consists of bidirectional LSTM with input taken from the 1st layer of BERT.
Efektivitas Penggunaan Game Analisis Training terhadap Peningkatan Kemampuan Berpikir Induktif Remaja

Inductive-reasoning is a step to draw general conclusions from individual matters. IR can be measured using the Intelligent Structure (IST) sub-ZR using a number pattern and using an Inductive Reasoning Test (IRT), which is an image pattern. One of the learning methods that are interesting and fun and student-cantered learning is Educational Game media. This study aims to see the effectiveness of games in learning to improve the students' inductive thinking skills at SMP YKPP Sungai Pakning. This study used a pre-experimental method with one group pretest-posttest conducted on 30 students of class IX B as the experimental subject. The data collection technique was implemented using a test instrument in 20 essay questions for IST and 30 objective questions for IRT. The material used in this game has also passed the validation stage carried out by a psychologist in Pekanbaru. Data were analyzed using the T-Test and score gain. Based on the study results, this game is believed to be 95% effective in improving adolescent inductive thinking skills, with an average increase of 26.6 points in the IST form and 17.8 points in the IRT form. Learning media with games are effectively applied to inductive-learning and can be used as an alternative to learning inductive-thinking at SMP YKPP Sungai Pakning.
Study of peripheral blood smear findings in patients of anemia and to compare it with automated hematology analyzer generated red cell parameters

Introduction: Anemia is one of the common medical conditions prevalent in our society. The correct categorization of varying types of anemia is essential for therapeutic purposes. Various laboratory tests are done to find out its underlying cause, but peripheral smear (PS) study of red blood cells (RBCs) morphology is important along with study of red blood cell indices in the classification of anemia.&#x0D; Objectives: This study was done to evaluate RBC morphology on peripheral smear examination in patients of anemia and to compare these findings with cell counter generated red blood cells indices comprising of Mean corpuscular volume (MCV), Mean corpuscular hemoglobin concentration (MCHC) and Mean corpuscular hemoglobin (MCH)&#x0D; Methodology: Red blood cell morphology on peripheral blood smear was studied in 350 patients of anemia irrespective of age and gender received under six months duration from 1st January 2019 to 30 th June 2019 and findings were compared with cell counter generated red cell parameters generated by automated hematology analyzer.&#x0D; Results: Most common anemia seen on smear examination was microcytic hypochromic anemia which accounted for 210 (60%) cases followed by dimorphic anemia in 73 (20.86%) cases. Most of the patients were female, comprising of 227 (64.85%) cases. Highest numbers of patient were in the age group of 21-30 years (20.30%). Sensitivity of MCV was 78%, MCHC was 14% and MCH was 80% in detection of microcytic hypochromic anemia. The sensitivity of MCV and MCH was found to be 100 % in detection of macrocytic anemia. Sensitivity of MCHC was only 10% for detection of macrocytic anemia. The sensitivity of MCV, MCHC and MCH was 78%, 100% and 67 % respectively in detection of normocytic normochromic anemia. &#x0D; Conclusion: The peripheral smear examination should always be interpreted along with the red blood cell indices generated by an automated analyzer in order to classify various types of anemia.
Noise Isn’t Always Negative: Countering Exposure Bias in Sequence-to-Sequence Inflection Models

Morphological inflection, like many sequence-to-sequence tasks, sees great performance from recurrent neural architectures when data is plentiful, but performance falls off sharply in lowerdata settings.We investigate one aspect of neural seq2seq models that we hypothesize contributes to overfitting -teacher forcing.By creating different training and test conditions, exposure bias increases the likelihood that a system too closely models its training data.Experiments show that teacher-forced models struggle to recover when they enter unknown territory.However, a simple modification to the training algorithm to more closely mimic test conditions creates models that are better able to generalize to unseen environments.
Lexical Features of Text Complexity: the case of Russian academic texts

The work presented in this paper is a part of an ongoing project that investigates academic text features indicative of its complexity at different grade levels. In this study we examine comparative complexity of Social science texts used in Russian secondary and high schools. Based on the metrics of ten descriptive and four lexical features assessed for seven classroom textbooks we claim lexical diversity, frequency, abstractness and the number of terminological units to be statistically significant predictors of text complexity. The total size of the Corpus of over 160.000 tokens comprising two sets of textbooks ranging from the 5 th to the 11 th grades provides a satisfactory level of its representativeness and as such a solid foundation for statistical validity of the results. We employ RusAC, an online text analyzer, to compute lexical features of texts and the effect of the four lexical features on text complexity is confirmed with a mixed analysis of variance. The study fills a gap both in corpus linguistics as regards a systematic approach to Russian academic texts and in text complexity studies as regards the description of secondary and high school textbooks.
Implementation of Feeder System to Support Monitoring the Potential Malnutrition

The case of malnutrition affecting many toddlers of Banjarmasin in isolated areas, remote areas, and remote villages. It is caused by the resultant of economic problems, facilities, infrastructures, and the lack of medical personnel. One of the ways undertaken by the government to overcome this problem is to carry out Posyandu activities. The posyandu program is organized to improve and monitor the nutrition of children under five. However, the reporting of posyandu data by each puskesmas is sometimes delayed because of the large amount of bureaucracy that must be passed to get to the center. Therefore a system feeder application is needed to monitor and mapping on the number of toddlers with potential malnutrition in the city of Banjarmasin.
DoTheMath at SemEval-2020 Task 12 : Deep Neural Networks with Self Attention for Arabic Offensive Language Detection

This paper describes our team work and submission for the SemEval 2020 (Sub-Task A) "Offensive Eval: Identifying and Categorizing Offensive Arabic Language in Arabic Social Media".Our two baseline models were based on different levels of representation: character vs. word level.In word level based representation we implemented a convolutional neural network model and a bi-directional GRU model.In character level based representation we implemented a hyper CNN and LSTM model.All of these models have been further augmented with attention layers for a better performance on our task.We also experimented with three types of static word embeddings: word2vec, FastText, and Glove, in addition to emoji embeddings, and compared the performance of the different deep learning models on the dataset provided by this task.The bi-directional GRU model with attention has achieved the highest score (0.85% F1 score) among all other models.
Multimodal Review Generation with Privacy and Fairness Awareness

Users express their opinions towards entities (e.g., restaurants) via online reviews which can be in diverse forms such as text, ratings, and images.Modeling reviews are advantageous for user behavior understanding which, in turn, supports various user-oriented tasks such as recommendation, sentiment analysis, and review generation.In this paper, we propose MG-PriFair, a multimodal neural-based framework, which generates personalized reviews with privacy and fairness awareness.Motivated by the fact that reviews might contain personal information and sentiment bias, we propose a novel differentially private (dp)-embedding model for training privacy guaranteed embeddings and an evaluation approach for sentiment fairness in the food-review domain.Experiments on our novel review dataset show that MG-PriFair is capable of generating plausibly long reviews while controlling the amount of exploited user data and using the least sentimentbiased word embeddings.To the best of our knowledge, we are the first to bring user privacy and sentiment fairness into the review generation task.
XD at SemEval-2020 Task 12: Ensemble Approach to Offensive Language Identification in Social Media Using Transformer Encoders

This paper presents six document classification models using the latest transformer encoders and a high-performing ensemble model for a task of offensive language identification in social media.For the individual models, deep transformer layers are applied to perform multi-head attentions.For the ensemble model, the utterance representations taken from those individual models are concatenated and fed into a linear decoder to make the final decisions.Our ensemble model outperforms the individual models and shows up to 8.6% improvement over the individual models on the development set.On the test set, it achieves macro-F1 of 90.9% and becomes one of the high performing systems among 85 participants in the sub-task A of this shared task.Our analysis shows that although the ensemble model significantly improves the accuracy on the development set, the improvement is not as evident on the test set.
KeyShield: A Scalable and Quantum-Safe Key Management Scheme

While encryption is powerful at protecting information, it critically relies upon the mystery/private cryptographic key's security. Poor key management would compromise any robust encryption algorithm. In this way, securing information is reduced to the issue of securing such keys from unauthorized access. In this work, KeyShield is proposed, a scalable and quantum-safe key management scheme. KeyShield provides the highest security level as it relies on the impossibility of finding a unique solution to an underdetermined linear system of equations. KeyShield achieves the rekeying using a single broadcast message, called a secure lock, in an open channel rather than pairwise secure channels. Security analyses for a list of attacks are provided, along with a detailed discussion on the quantum-safe feature. KeyShield outperforms state-of-the-art schemes in several aspects, including quantum-resistance, computation cost, message overhead, storage cost, and rekeying delay.
UAlberta at SemEval-2020 Task 2: Using Translations to Predict Cross-Lingual Entailment

We investigate the hypothesis that translations can be used to identify cross-lingual lexical entailment.We propose novel methods that leverage parallel corpora, word embeddings, and multilingual lexical resources.Our results demonstrate that the implementation of these ideas leads to improvements in predicting entailment.
MeisterMorxrc at SemEval-2020 Task 9: Fine-Tune Bert and Multitask Learning for Sentiment Analysis of Code-Mixed Tweets

Natural language processing (NLP) has been applied to various fields including text classification and sentiment analysis.In the shared task of sentiment analysis of code-mixed tweets, which is a part of the SemEval-2020 competition (Patwa et al., 2020), we preprocess datasets by replacing emoji and deleting uncommon characters and so on, and then fine-tune the Bidirectional Encoder Representation from Transformers(BERT) to perform the best.After exhausting top3 submissions, Our team MeisterMorxrc achieves an averaged F1 score of 0.730 in this task, and and our codalab username is MeisterMorxrc
A Robust Digital Watermarking Algorithm Based on Finite-Set Discrete Radon Transform Tight Frame

Digital watermarking technology plays a powerful role in the effective protection of digital media copyright, image authentication, image sharing, image information transmission and other fields.Driven by strong demand, digital image watermarking technology has aroused widespread research interest and has gradually developed into one of the most active research directions in information science.In this paper, we present a novel robust digital watermarking algorithm based on discrete radon transform tight frame in finite-set (FDRT).FDRT of the zero mean image is a tight frame, the frame boundary A = B = 1, the dual of the frame is itself.The decomposition and reconstruction of the FDRT tight frame will not cause the phenomenon of image distortion.The embedding of hidden watermark is to add a weak signal to the strong background of the original image.Watermark extraction is to effectively identify the embedded weak signal.The feasibility of the watermarking algorithm is analyzed from two aspects of information hiding and robustness.We select the independent Gaussian random vector as the watermark series, and the peak signal-to-noise ratio (PSNR) as the visual degradation criterion of the watermark image.Basing the FDRT compact stand dual operator, we derived the relationship among the strength parameter, square sum of watermark series, the PSNR.Using Checkmark system, the simulation results show that the algorithm is robust enough to some very important image processing attacks such as lossy compression, MAP, filtering, segmentation, edge enhancement, jitter, quadratic modulation and general geometric attack (scaling, rotation, shearing), etc.
To What Degree Can Language Borders Be Blurred In BERT-based Multilingual Spoken Language Understanding?

This paper addresses the question as to what degree a BERT-based multilingual Spoken Language Understanding (SLU) model can transfer knowledge across languages. Through experiments we will show that, although it works substantially well even on distant language groups, there is still a gap to the ideal multilingual performance. In addition, we propose a novel BERT-based adversarial model architecture to learn language-shared and language-specific representations for multilingual SLU. Our experimental results prove that the proposed model is capable of narrowing the gap to the ideal multilingual performance.
Correction to: Biostratigraphy of the Kazhdumi Formation (Albian), northeast of Shiraz, Zagros Basin (SW of Iran)

The article "Biostratigraphy of the Kazhdumi Formation (Albian), northeast of Shiraz, Zagros Basin (SW of Iran)”.