Reviewerly/exHarmony huggingface dataset:
COMPARISON OF FORECASTING VIOLENCE CASES NUMBER AGAINST WOMEN AND CHILDREN USING DOUBLE EXPONENTIAL SMOOTHING (DES) AND AUTO REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) METHODS

Violence is something that is being widely discussed. It is due to the increasing number of victims of violence in a scope where victims should feel safe. Therefore, the researchers took this case intending to predict the number of violence cases against women and children in Jakarta so that the government can anticipate the spike in cases and evaluate the policies that will be issued in this case. The data used was from the Office for the Empowerment of Child Protection and Population Control (DPPAPP) of DKI Jakarta Province from January 2018 to October 2021 to predict the number of cases in 2022. Based on the analysis results, it is known that the number of cases of violence against women and children has decreased throughout 2022. In addition, the accuracy of the model using the Double Exponential Smoothing (DES) method is 44.91%, and the Auto-Regressive Integrated Moving Average (ARIMA) is 39.03%.
DEVELOPMENT OF ANDROID-BASED “MBARENGI” STATISTICS E-MODULE AS AN INNOVATION FOR STATISTICS LEARNING MEDIA WITH HYBRID LEARNING

The learning process is closely related to the interaction between educators and students. In order to facilitate the learning process, educators are required to make learning more innovative in order to be able to encourage students to study hard and be able to follow the learning process well. One of the innovations that can be done in the learning process is innovation in learning media. Learning media is one of the components of learning that has an important role in teaching and learning activities, especially in the transition after the Covid 19 pandemic. The use of smartphones can be one of the innovations of learning media by using Android-based e-modules. This research is development research or called Research and Development (R&amp;D) using the ADDIE development model (Analyze, Design, development, Implementation, and Evaluation). The Android-based "MBARENGI" Statistics E-Module was validated by material experts, learning media experts, and tested on a limited scale sample. The results of the validation of material experts on the aspect of content material are 91.5% and on the language aspect are 93.2%. The results of media expert validation on the aspect of content quality are 92.3%, on design and audio aspects are 94.4% and the interaction and feedback aspects are 92.3%. In the test results, student responses obtained an average result are 88.8% which stated that the Android-based "MBARENGI" Statistics E-Module was feasible to be used in the learning process.
EL-RFSVM: An Ensemble Learning Framework Based on Support Vector Machine and Random Forests for Labour Resource Allocation

The classical support vector machine (SVM) algorithm proposes only a two-class classification algorithm, but in the practical application of data mining, it is generally necessary to solve the classification problem of multiple classes. At the same time, for small data or low-dimensional data (data with few features), random forests do not produce good classifications. To address the limitations of single prediction models, this paper proposes a combined prediction model based on EL-RFSVM in the ensemble learning framework. This paper chooses a publicly available dataset of university students as the representative for labour allocation experiments. In this paper, we build two single prediction models, support vector machine model and random forest, and use the nonlinear mapping capability of the random forest to adjust the weight coefficients. The weights of the single model were determined to the two models, SVM and RF, and obtain the predicted labour allocation values. Finally, the performance of the three models on the problem is evaluated using AUC. The experimental results show that our proposed model has better prediction results compared to the single prediction model.
ANALYZING CUSTOMERS’ SATISFACTION ON FACILITY MANAGEMENT SYSTEM (FMS) USING WEBQUAL 4.0: A CASE STUDY

Propertek Facility Management System is a website-based ERP, which is used as a dashboard and is systematically integrated with an application called Many Option. Currently the Property Management Facility Management System has been used in 3 apartments, namely The Pakubuwono Residence, Gading Icon Apartment, and Kubikahomy. This apartment has a total of 2,578 tenants to date. Based on this data, the quality of applications used by tenants must be measured based on user satisfaction. This study aims to determine whether the variables in the Webqual 4.0 measurement method, namely usability, information quality, and service interaction quality, can affect the level of user satisfaction of the Propertek Facility Management System website. The research method used is a quantitative research method, quantitative data is a research method based on positivistic (concrete data), research data in the form of numbers to be measured using statistics as a test calculation tool, related to the problem under study to produce a conclusion. In this study, the quality of the Property Management Facility Management System was measured using the WebQual 4.0 method. WebQual 4.0 is a method of measuring website quality based on end user perceptions. Webqual 4.0 was compiled based on research on three areas or dimensions, namely Usability, Information Quality, and Service Interaction. The results of measuring website quality using the WebQual 4.0 method are expected to be information for the development team to find out system weaknesses, as well as become a reference and benchmark for improving system quality.
Broadcast Studio Development for Supporting Online Learning and Community Counselling

Universities have a role and function to educate the community in addition to conducting lectures. One form of activities from this role is to provide information to the community in the form of providing guidance, sharing knowledge or training. With the advancement of internet technology today, this can be done through online platform. However, this must be supported by qualified equipment in order to gain interesting and acceptable outcomes for the community as well as students. In this service, a studio that can be used to support teaching activities and community counselling for academic community in Politeknik Negeri Bandung is created. The making of this broadcast studio is divided into three stages. The first is the making of a three-dimensional model of the room using 3D modelling software. The second stage is the civil work to build a broadcast studio. The last stage is the completion of the broadcast studio with supporting equipment for video content creation. The results obtained are quite satisfactory. From the majority of answers obtained from the survey, students find this studio useful, quite comfortable to use and worthy of creating video content. For the audio section, the correspondents believe that this aspect still needs to be improved, while the room layout and visual equipment are considered good and attractive.
Specializing Pre-trained Language Models for Better Relational Reasoning via Network Pruning

Pretrained masked language models (PLMs) were shown to be inheriting a considerable amount of relational knowledge from the source corpora.In this paper, we present an in-depth and comprehensive study concerning specializing PLMs into relational models from the perspective of network pruning.We show that it is possible to find subnetworks capable of representing grounded commonsense relations at non-trivial sparsity while being more generalizable than original PLMs in scenarios requiring knowledge of single or multiple commonsense relations.
Felix&amp;Julia at SemEval-2022 Task 4: Patronizing and Condescending Language Detection

This paper describes the authors’ submission to the SemEval-2022 task 4: Patronizing and Condescending Language (PCL) Detection. The aim of the task is the detection and classification of PCL in an annotated dataset. Subtask 1 includes a binary classification task (PCL or not PCL). Subtask 2 is a multi label classification task where the system identifies different categories of PCL. The authors of this paper submitted two different models: one RoBERTa model and one DistilBERT model. Both systems performed better than the random and RoBERTA baseline given by the task organizers. The RoBERTA model finetuned by the authors performed better in both subtasks than the DistilBERT model.
Flowstorm: Open-Source Platform with Hybrid Dialogue Architecture

This paper presents a conversational AI platform called Flowstorm.Flowstorm is an opensource SaaS project suitable for creating, running, and analyzing conversational applications.Thanks to the fast and fully automated build process, the dialogues created within the platform can be executed in seconds.Furthermore, we propose a novel dialogue architecture that uses a combination of tree structures with generative models.The tree structures are also used for training NLU models suitable for specific dialogue scenarios.However, the generative models are globally used across applications and extend the functionality of the dialogue trees.Moreover, the platform functionality benefits from out-of-the-box components, such as the one responsible for extracting data from utterances or working with crawled data.Additionally, it can be extended using a custom code directly in the platform.One of the essential features of the platform is the possibility to reuse the created assets across applications.There is a library of prepared assets where each developer can contribute.All of the features are available through a user-friendly visual editor.
QiNiAn at SemEval-2022 Task 5: Multi-Modal Misogyny Detection and Classification

In this paper, we describe our submission to the misogyny classification challenge at SemEval-2022.We propose two models for the two subtasks of the challenge: The first uses joint image and text classification to classify memes as either misogynistic or not.This model uses a majority voting ensemble structure built on traditional classifiers and additional image information such as age, gender and nudity estimations.The second model uses a RoBERTa classifier on the text transcriptions to additionally identify the type of problematic ideas the memes perpetuate.Our submissions perform above all organizer submitted baselines.For binary misogyny classification, our system achieved the fifth place on the leaderboard, with a macro F1-score of 0.665.For multi-label classification identifying the type of misogyny, our model achieved place 19 on the leaderboard, with a weighted F1-score of 0.637.
CULG: Commercial Universal Language Generation

Pre-trained language models (PLMs) have dramatically improved performance for many natural language processing (NLP) tasks in domains such as finance and healthcare.However, the application of PLMs in the domain of commerce, especially marketing and advertising, remains less studied.In this work, we adapt pretraining methods to the domain of commerce, by proposing CULG, a large-scale commercial universal language generation model which is pre-trained on a corpus drawn from 10 markets across 7 languages.We propose 4 commercial generation tasks and a two-stage training strategy for pre-training, and demonstrate that the proposed strategy yields performance improvements on three generation tasks as compared to single-stage pre-training.Extensive experiments show that our model outperforms other models by a large margin on commercial generation tasks.
Collecting high-quality adversarial data for machine reading comprehension tasks with humans and models in the loop

We present our experience as annotators in the creation of high-quality, adversarial machinereading-comprehension data for extractive QA for Task 1 of the First Workshop on Dynamic Adversarial Data Collection (DADC).DADC is an emergent data collection paradigm with both models and humans in the loop.We set up a quasi-experimental annotation design and perform quantitative analyses across groups with different numbers of annotators focusing on successful adversarial attacks, cost analysis, and annotator confidence correlation.We further perform a qualitative analysis of our perceived difficulty of the task given the different topics of the passages in our dataset and conclude with recommendations and suggestions that might be of value to people working on future DADC tasks and related annotation interfaces.
SkoltechNLP at SemEval-2022 Task 8: Multilingual News Article Similarity via Exploration of News Texts to Vector Representations

This paper describes our contribution to Se-mEval 2022 Task 8 on Multilingual News Article Similarity.The aim was to test completely different approaches and distinguish the best performing.That is why we've considered systems based on Transformer-based encoders, NER-based, and NLI-based methods (and their combination with SVO dependency triplets representation).The results prove that Transformer models produce the best scores.However, there is space for research and approaches that give not yet comparable but more interpretable results.
OversampledML at SemEval-2022 Task 8: When multilingual news similarity met Zero-shot approaches

We investigate the capabilities of pre-trained models without any fine-tuning, for a document-level multilingual news similarity task of SemEval-2022.We utilize title and news content with appropriate pre-processing techniques.Our system derives 14 different similarity features using a combination of pre-trained MPNet with well-known statistical methods (i.e.TF-IDF, Word Mover's distance).We formulate the multilingual news similarity task as a regression task and approximate the overall similarity between two news articles using these features.Our best performing system achieved a correlation score of 70.1% and was ranked 20 th among the 34 participating teams.In this paper, in addition to a system description, we also provide further analysis of our results and an ablation study highlighting the strengths and limitations of our features.We make our code publicly available at https://github.com/cicliscl/multinewssimilarity.
Does Pre-training Induce Systematic Inference? How Masked Language Models Acquire Commonsense Knowledge

Transformer models pre-trained with a maskedlanguage-modeling objective (e.g., BERT) encode commonsense knowledge as evidenced by behavioral probes; however, the extent to which this knowledge is acquired by systematic inference over the semantics of the pretraining corpora is an open question.To answer this question, we selectively inject verbalized knowledge into the pre-training minibatches of BERT and evaluate how well the model generalizes to supported inferences after pre-training on the injected knowledge.We find generalization does not improve over the course of pre-training BERT from scratch, suggesting that commonsense knowledge is acquired from surface-level, co-occurrence patterns rather than induced, systematic reasoning.
Nowruz at SemEval-2022 Task 7: Tackling Cloze Tests with Transformers and Ordinal Regression

This paper outlines the system using which team Nowruz participated in SemEval 2022 Task 7 "Identifying Plausible Clarifications of Implicit and Underspecified Phrases" for both subtasks A and B (Roth et al., 2022).Using a pre-trained transformer as a backbone, the model targeted the task of multi-task classification and ranking in the context of finding the best fillers for a cloze task related to instructional texts on the website Wikihow.The system employed a combination of two ordinal regression components to tackle this task in a multi-task learning scenario.According to the official leaderboard of the shared task, this system was ranked 4th in both classification and ranking subtasks out of 21 participating teams.With additional experiments, the models have since been further optimised.
Free speech or Free Hate Speech? Analyzing the Proliferation of Hate Speech in Parler

Social platforms such as Gab and Parler, branded as 'free-speech' networks, have seen a significant growth of their user base in recent years.This popularity is mainly attributed to the stricter moderation enforced by mainstream platforms such as Twitter, Facebook, and Reddit.In this work we provide the first large scale analysis of hate-speech on Parler.We experiment with an array of algorithms for hate-speech detection, demonstrating the limitations of transfer learning in that domain, given the illusive and ever changing nature of the ways hate-speech is delivered.In order to improve classification accuracy we annotated 10K Parler posts, which we use to fine-tune a BERT classifier.Classification of individual posts is then leveraged for the classification of millions of users via label propagation over the social network.Classifying users by their propensity to disseminate hate, we find that hate mongers make about 16% of Parler active users, and that they have distinct characteristics comparing to other user groups.We find that hate mongers are more active, more central, express distinct levels of sentiment, and convey a distinct array of emotions like anger and sadness.We further complement our analysis by comparing the trends observed in Parler to those found in Gab.To the best of our knowledge, this is among the first works to analyze hate speech in Parler in a quantitative manner and on the user level.
SemEval-2022 Task 3: PreTENS-Evaluating Neural Networks on Presuppositional Semantic Knowledge

We report the results of the SemEval 2022 Task 3, PreTENS, on evaluation the acceptability of simple sentences containing constructions whose two arguments are presupposed to be or not to be in an ordered taxonomic relation.The task featured two sub-tasks articulated as: (i) binary prediction task and (ii) regression task, predicting the acceptability in a continuous scale.The sentences were artificially generated in three languages (English, Italian and French).21 systems, with 8 system papers were submitted for the task, all based on various types of fine-tuned transformer systems, often with ensemble methods and various data augmentation techniques.The best systems reached an F1-macro score of 94.49 (sub-task1) and a Spearman correlation coefficient of 0.80 (sub-task2), with interesting variations in specific constructions and/or languages.
How to Translate Your Samples and Choose Your Shots? Analyzing Translate-train &amp; Few-shot Cross-lingual Transfer

Translate-train or few-shot cross-lingual transfer can be used to improve the zero-shot performance of multilingual pretrained language models.Few-shot utilizes high-quality lowquantity samples (often manually translated from the English corpus).Translate-train employs a machine translation of the English corpus, resulting in samples with lower quality that could be scaled to high quantity.Given the lower cost and higher availability of machine translation compared to manual professional translation, it is important to systematically compare few-shot and translate-train, understand when each has an advantage, and investigate how to choose the shots to translate in order to increase the few-shot gain.This work aims to fill this gap: we compare and quantify the performance gain of few-shot vs. translate-train using three different base models and a varying number of samples for three tasks/datasets (XNLI, PAWS-X, XQuAD) spanning 17 languages.We show that scaling up the training data using machine translation gives a larger gain compared to using the small-scale (higher-quality) few-shot data.When few-shot is beneficial, we show that there are random sets of samples that perform better across languages and that the performance on English and on the machine-translation of the samples can both be used to choose the shots to manually translate for an increased few-shot gain. 1
TUG-CIC at SemEval-2021 Task 6: Two-stage Fine-tuning for Intended Sarcasm Detection

We present our systems and findings for the iSarcasmEval: Intended Sarcasm Detection In English and Arabic at SEMEVAL 2022.Specifically we take part in the Subtask A for the English language.The task aims to determine whether a text from social media (a tweet) is sarcastic or not.We model the problem using knowledge sources, a pre-trained language model on sentiment/emotion data and a dataset focused on intended sarcasm.Our submission ranked third place among 43 teams.In addition, we show a brief error analysis of our best model to investigate challenging examples for detecting sarcasm.
SATLab at SemEval-2022 Task 4: Trying to Detect Patronizing and Condescending Language with only Character and Word N-grams

A logistic regression model only fed with character and word n-grams is proposed for the SemEval-2022 Task 4 on Patronizing and Condescending Language Detection (PCL).It obtained an average level of performance, well above the performance of a system that tries to guess without using any knowledge about the task, but much lower than the best teams.To facilitate the interpretation of the performance scores, the F1 measure, the best level of performance of a system that tries to guess without using any knowledge is calculated and used to correct the F1 scores in the manner of a Kappa.As the proposed model is very similar to the one that performed well on a task requiring to automatically identify hate speech and offensive content, this paper confirms the difficulty of PCL detection.
Multilinguals at SemEval-2022 Task 11: Transformer Based Architecture for Complex NER

We investigate the task of complex NER for the English language.The task is non-trivial due to the semantic ambiguity of the textual structure and the rarity of occurrence of such entities in the prevalent literature.Using pretrained language models such as BERT, we obtain a competitive performance on this task.We qualitatively analyze the performance of multiple architectures for this task.All our models are able to outperform the baseline by a significant margin.Our best performing model beats the baseline F1-score by over 9%.
EmojiCloud: a Tool for Emoji Cloud Visualization

This paper proposes EmojiCloud, an opensource Python-based emoji cloud visualization tool, to generate a quick and straightforward understanding of emojis from the perspective of frequency and importance.EmojiCloud is flexible enough to support diverse drawing shapes, such as rectangles, ellipses, and image masked canvases.We also follow inclusive and personalized design principles to cover the unique emoji designs from seven emoji vendors (e.g., Twitter, Apple, and Windows) and allow users to customize plotted emojis and background colors.We hope EmojiCloud can benefit the whole emoji community due to its flexibility, inclusiveness, and customizability.
Contrastive Explanations of Text Classifiers as a Service

The recent growth of black-box machinelearning methods in data analysis has increased the demand for explanation methods and tools to understand their behaviour and assist human-ML model cooperation.In this paper, we demonstrate ContrXT , a novel approach that uses natural language explanations to help users to comprehend how a back-box model works.ContrXT provides time contrastive (tcontrast) explanations by computing the differences in the classification logic of two different trained models and then reasoning on their symbolic representations through Binary Decision Diagrams.ContrXT is publicly available at ContrXT.ai as a python pip package.
Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting Patronising and Condescending Language

This work describes the development of different models to detect patronising and condescending language within extracts of news articles as part of the SemEval 2022 competition (Task-4).This work explores different models based on the pre-trained RoBERTa language model coupled with LSTM and CNN layers.The best models achieved 15 th rank with an F1-score of 0.5924 for subtask-A and 12 th in subtask-B with a macro-F1 score of 0.3763.