Reviewerly/exHarmony huggingface dataset:
Mobile Game Development for Children with Down Syndrome

Children with diagnosis of down syndrome has different kinds of personalities, mental states, and talents similar to other children. The key point is that every child needs proper support at the right time. There are options like special education programs, physical and other therapies, and play groups. However, there are not any electronic games to support the education and improvement of these children. The goal of this study is to develop a mobile game for children with down syndrome, in order to help them learn everyday knowledge by using a mobile device such as a smart phone or a tablet. In most cases pedagogues use puzzle or paper based card games to teach these children the necessary knowledge they need to maintain their lives. This study aims to optimize the learning process of children with down syndrome by providing educators electronic games they can use to teach them in a mobile application which will enrich the teaching process in addition to update their teaching methods to Industry 4.0 standards.
Reed at SemEval-2020 Task 9: Fine-Tuning and Bag-of-Words Approaches to Code-Mixed Sentiment Analysis

We explore the task of sentiment analysis on Hinglish (code-mixed Hindi-English) tweets as participants of Task 9 of the SemEval-2020 competition, known as the SentiMix task.We had two main approaches: 1) applying transfer learning by fine-tuning pre-trained BERT models and 2) training feedforward neural networks on bag-of-words representations.During the evaluation phase of the competition, we obtained an F-score of 71.3% with our best model, which placed 4 th out of 62 entries in the official system rankings.
NGO Support Software Solution: for effective reachability

In India there are currently a lot of NGOs working for noble causes.Citizens are also eager to contribute.Unfortunately for a lot of these NGOs there is a shortage or absence of IT infrastructure, hindering their reach and effectiveness.We aim to aid such NGOs and provide them with the necessary IT infrastructure to optimize use of resources and increase their reach for food and money donations.The project includes a cross platform mobile application which will help them manage their volunteers, get orders by spreading awareness through a social media module to connect to people who wish to support them in this noble cause.There are many freelancing developers and existing apps, the goal is to extract all the best features, figure out best platforms, harness latest trends and develop the app at a cost that the NGO can afford.Our literature survey of existing app gave insights about designing the necessary modules that we must have.To have the analysis of people's attitude, habits and trends, studying papers related to social media trends inspired us to harness its power.The study of other kinds of systems using mobile applications, encouraged us to consider options of 100% cost free background, enabling us to generate an economical solution.
Generative Fashion for Indian Clothing

Deep learning-based innovations, particularly GANs, have recently shown great success in fashion modelling for various use cases such as pose and face generation. A famous work, FashionGAN[1], can generate images with modified clothing as per natural language description and uses the DeepFashion dataset, which primarily contains clothing styles of the Western countries. Currently, no dataset caters to Indian style and clothing. Hence, we present a dataset of 12k images and descriptions pertaining to the Indian culture as well as a baseline approach with this work. Deep learning-based innovations in the Indian Fashion context are a relatively new area of research, and we hope our work will be a starting point for other researchers. Code and Dataset: https://github.com/ronakkaoshik42/Generative_fashion
YNU-oxz at SemEval-2020 Task 5: Detecting Counterfactuals Based on Ordered Neurons LSTM and Hierarchical Attention Network

This paper describes the system and results of our team's participation in SemEval-2020 Task5: Modelling Causal Reasoning in Language: Detecting Counterfactuals, which aims to simulate counterfactual semantics and reasoning in natural language.This task contains two subtasks: Subtask1-Detecting counterfactual statements and Subtask2-Detecting antecedent and consequence.We only participated in Subtask1, aiming to determine whether a given sentence is counterfactual.In order to solve this task, we proposed a system based on Ordered Neurons LSTM (ON-LSTM) with Hierarchical Attention Network (HAN) and used Pooling operation for dimensionality reduction.Finally, we used the K-fold approach as the ensemble method.Our model achieved an F1 score of 0.7040 in Subtask1 (Ranked 16/27).
Apakah Youtuber Indonesia Kena Bully Netizen?

This study will examine the cyberbullying phenomenon that was experienced by Indonesian Youtubers in their Instagram comment section. Cyberbullying is the use of electronic communication to bully a person, typically by sending messages of an intimidating or threatening nature. Youtubers are the subject of this research due to their massive following, who constantly responds to every content posted on their Instagram page. The algorithm chosen to conduct this sentiment analysis was Support Vector Machine (SVM) due to their high accuracy percentage. The data used in this analysis was retrieved from 10 Indonesian Youtuber Instagram accounts. In order to analyze this data, several step was done including text mining, data cleansing, data modeling and applying model to test data. The result of analysis using an SVM model with an accuracy of 81.2% is 49.524% of comments on an Indonesian Youtuber comment section are considered as cyberbullying.
Forgery Protection of Academic Certificates through Integrity Preservation at Scale using Ethereum Smart Contract

Academic credentials are precious assets as they form an evidence for one’s identity and eligibility. Fraud inissuance and verification of academic certificates have been a long-standing issue in academic community. Due to lack of antiforgery mechanisms there has been substantial increase in fraudulent certificates. The need of the hour is to have a transparent and reliable model for issuing and verifying academic certificates to eliminate fraud in the process. Decentralized, Auditable and Tamper-proof properties of Blockchain makes it possibly the best choice for issuing and verifying academic certificates. In this paper we propose a model, where regulatory body authorizes higher education Institutes (universities and colleges) for issuing academic certificates to students in a decentralized way. Anyone in the world can verify the authenticity of the certificate by triggering appropriate smart contract functions, thus eliminating any possibility of fraud in the process. In addition we used multi signature scheme where certificates are required to be signed by designated authority from Higher Education Institutes, thus allowing for multi-level checks on certificate contents before being successfully deployed on Blockchain. We have also provide Proof of Concept in Ethereum Blockchain and evaluated its performance in terms of cost, security and scalability.
“Clavis Aurea”: An augmented Reality Game for the Teaching of Local History

The aim of this study was the design, development, and evaluation of an Augmented Reality game to teach students about the local history of a Greek island. Design-based research served as the foundation of this study’s methodology. Experts in ICT in education, teachers with knowledge of the local history and primary education students participated in the evaluation of this study. The results of the evaluation showed that the game presents satisfactory levels of usability and that its content is compatible with the island’s local history. Among the factors influencing its use by students were internet connectivity, the large number of visitors at the archaeological site where the game was played, and the problem of coordinating the student groups.
Inflating Topic Relevance with Ideology: A Case Study of Political Ideology Bias in Social Topic Detection Models

We investigate the impact of political ideology biases in training data.Through a set of comparison studies, we examine the propagation of biases in several widely-used NLP models and its effect on the overall retrieval accuracy.Our work highlights the susceptibility of large, complex models to propagating the biases from human-selected input, which may lead to a deterioration of retrieval accuracy, and the importance of controlling for these biases.Finally, as a way to mitigate the bias, we propose to learn a text representation that is invariant to political ideology while still judging topic relevance.
Technological transition from analog to internet of things ( IoT ) on Indian petro-retail’s customer service

Objectives: To identify the gap in the adaptation of IoTs towards meeting the customer expectation for services in petro-retailing in India. Method: Review of available published research articles, case studies, published article in journals and websites on uses of IOTs in various sectors like retail, telecom manufacturing, supply chain management, oil &amp; gas etc. Findings: Technological advances have had a significant impact on both consumers as well as industries. While internet changed the way human interact, Internet of Things (IoT) is expected to change the way machines interact. Adding a networked layer to regular objects has transformed the way we use and interact with them. Industries like healthcare, manufacturing, telecom, etc have seen significant changes with the introduction of IoT. Oil &amp; Gas industry has also seen implementation of IoT but the adoption has varied according to sector. Upstream sector, involved in exploration and drilling, has seen significant adoption while Mid-stream sector, involved in transport and storage, has seen only specific application. IoT solutions have been used for cost optimization as well as improved operations management. Downstream sector (including refining and retail) has seen only limited adoption. Novelty: IoTs are the technology on which future society is going to ride. The use of IoT is going to have huge impact in service industry including petro retailing. Hence the analysis of gap in adaptation shall be helpful for the academician’s, managers in Oil &amp; Gas industry for further review, study and implementation. Keywords: Internet of Things; Indian fuel retail; IOT in oil &amp; gas; new technology adoption; customer experience
Amsqr at SemEval-2020 Task 12: Offensive Language Detection Using Neural Networks and Anti-adversarial Features

This paper describes a method and system to solve the problem of detecting offensive language in social media using anti-adversarial features.Our submission to the SemEval-2020 task 12 challenge was generated by an stacked ensemble of neural networks fine-tuned on the OLID dataset and additional external sources.For Task-A (English), text normalisation filters were applied at both graphical and lexical level.The normalisation step effectively mitigates not only the natural presence of lexical variants but also intentional attempts to bypass moderation by introducing out of vocabulary words.Our approach provides strong F1 scores for both 2020 (0.
Online Product Reviews and Their Impact on Third Party Sellers Using Natural Language Processing

The purpose of this paper is to gain insights from the online product reviews of e-commerce sites such as Flipkart and Amazon and analyze its impact on third party sellers. To judge the authenticity of a product, reviews are more useful than ratings, since ratings do not give a complete picture. It is always preferred to consider both the product and seller reviews to have a seamless delivery and defect less product. In this paper, natural processing methods are used to gain insights by considering online reviews of a product. Methods such as sentiment analysis, bag of words model help to understand the impact of online product reviews on the seller's ratings and their performance over some time. The reviews are categorized into positive, negative, and neutral using sentiment analysis. Further, topic modeling is done to find out the topic reviews are majorly referring to. The seller reviews for a specific product after analysis are compared with the overall seller reviews to judge the authenticity. The results of this paper would be beneficial to both the consumers and sellers.
Ontology Meter for Twitter Fake Accounts Detection

One of the most popular social media platforms, Twitter is used by millions of people to share information, broadcast tweets, and follow other users. Twitter is an open application programming interface and thus vulnerable to attack from fake accounts, which are primarily created for advertisement and marketing, defamation of an individual, consumer data acquisition, increase fake blog or website traffic, share disinformation, online fraud, and control. Fake accounts are harmful to both users and service providers, and thus recognizing and filtering out such content on social media is essential. This study presents a new approach to detect fake Twitter accounts using ontology and Semantic Web Rule Language (SWRL) rules. SWRL rules-based reasoner is utilized under predefined rules to infer whether the profile is trust or fake. This approach achieves a high detection accuracy of 97%. Furthermore, ontology classifier is an interpretable model that offers straightforward and human-interpretable decision rules.
A Survey of the Online Learning Implementation During COVID-19 Outbreak

This paper aim is to conduct a literature review of online learning implementation due to the impact of the COVID-19 outbreak. Specifically, the discussion will focus on the conditions of people, the facilities and technology used, and the process to support the success of online learning. The methodology of research adapted from systematic literature review method, namely PRISMA. As the result, we found as many 21 related works that explained people, technology and process aspect to implement online learning systems successfully. The main issues of online learning during COVID-19 is the availability of hardware (computer, phone, and so forth), software (Google Apps, Moodle, and so on) and internet connection. The list of tools recommendation for supporting activities of online learning is also discussed in this research.
Application of the Reccommended Learning Path in the Personalized Adaptive E-learning System

The study analyses how the topic sequences (teacher, learner, optimal) proposed in the personalized adaptive e-learning system influence the learning outcomes of the course.Three experiments were organised within the study.The first experiment showed that in most cases higher grades got those students who used the learner topic sequence.That lead to the need for improving the method for obtaining the optimal topic sequence.In the second experiment, the importance and complexity of the course topics was assessed.Next, weights for each topic were calculated based on the grade, importance and complexity of the topic.A recommended learning path development was proposed.The third experiment of the study evaluated the recommended learning path.The results showed that the recommended learning path gave better learning outcomes for two topics when compared to the optimal topic sequence.Future research will focus on the recommended learning path validation using larger sample group.
Hierarchical Trivia Fact Extraction from Wikipedia Articles

Recently, automatic trivia fact extraction has attracted much research interest.Modern search engines have begun to provide trivia facts as the information for entities because they can motivate more user engagement.In this paper, we propose a new unsupervised algorithm that automatically mines trivia facts for a given entity.Unlike previous studies, the proposed algorithm targets at a single Wikipedia article and leverages its hierarchical structure via top-down processing.Thus, the proposed algorithm offers two distinctive advantages: it does not incur high computation time, and it provides a domain-independent approach for extracting trivia facts.Experimental results demonstrate that the proposed algorithm is over 100 times faster than the existing method which considers Wikipedia categories.Human evaluation demonstrates that the proposed algorithm can mine better trivia facts regardless of the target entity domain and outperforms the existing methods.
Forest Fire Prediction in Northern Sumatera using Support Vector Machine Based on the Fire Weather Index

Forest fires are a major environmental issue, creating economical and ecological damage while dangering human lives. The investigation and survey for forest fire had been done in Aek Godang, Northern Sumatera, Indonesia. There is 26 hotspot in 2017 close to Aek Godang, North Sumatera, Indonesia. In this study, we use a data mining approach to train and test the data of forest fire and the Fire Weather Index (FWI) from meteorological data. The aim of this study to predict the burned area and identify the forest fire in Aek Godang areas, North Sumatera. The result of this study indicated that Fire fighting and prevention activity may be one reason for the observed lack of correlation. The fact that this dataset exists indicates that there is already some effort going into fire prevention.
Polarity Imbalance in Lexicon-based Sentiment Analysis

Polarity imbalance is an asymmetric situation that occurs while using parametric threshold values in lexicon-based Sentiment-Analysis (SA). The variation across the thresholds may have an opposite impact on the prediction of negative and positive polarity. We hypothesize that this may be due to asymmetries in the data or in the lexicon, or both. We carry out therefore experiments for evaluating the effect of lexicon and of the topics addressed in the data. Our experiments are based on a weighted version of the Italian linguistic resource MAL (Morphologically-inflected Affective Lexicon) by using as weighting corpus TWITA, a large-scale corpus of messages from Twitter in Italian. The novel Weighted-MAL (W-MAL), presented for the first time int this paper, achieved better polarity classification results especially for negative tweets, along with alleviating the aforementioned polarity imbalance.
Adding geodesic information and stochastic patch-wise image prediction for small dataset learning

Most recent methods of image augmentation and prediction are building upon the deep learning paradigm. A careful preparation of the image dataset and the choice of a suitable network architecture are crucial steps to assess the desired image features and, thence, achieve accurate predictions. We first propose to help the learning process by adding structural information with specific distance transform to the input image data. To handle cases with limited number of training samples, we propose a patch-based procedure with a stratified sampling method at inference. We validate our approaches on two image datasets, corresponding to two different tasks. The ability of our method to segment and predict images is investigated through the ISBI 2012 segmentation challenge dataset and generated electric field masks, respectively. The obtained results are evaluated using appropriate metrics: VRand for image segmentation and SSIM, UIQ and PSNR for image prediction. The proposed techniques demonstrate that the established framework is a reliable estimation method that could be used for a wide range of applications.
Deep &amp; Attentional Crossing Network for Click-through Rate Prediction

The problem of Click-through rate(CTR) prediction is the core issue to many real-world applications such as online advertising and recommendation systems. An effective prediction relies on high-order combinatorial features, which are often hand-crafted by experts. Limited by human experience and high implementation costs, combinatorial features cannot be manually captured thoroughly and comprehensively. There have been efforts in improving hand-crafted features automatically by designing feature-generating models such as FMs, DCN, and so on. Despite the great success of these structures, most of the existing models cannot differentiate the high-quality feature interactions from the huge amount of useless feature interactions, which can easily impair their performance. In this paper, we propose a Higher-Order Attentional Network(HOAN) to select high-quality combinatorial features. HOAN is a hierarchical structure, the multiple crossing layers can learn feature interactions of any order in an end-toend manner. Inside the crossing layer, each interaction item has its unique weight with consideration of global information to eliminate useless features and select high-quality features. Besides, HOAN also maintains the integrity of individual feature embedding and offers interpretive feedback to the calculating process. Furthermore, we combine DNN and HOAN, proposing a Deep &amp; Attentional Crossing Network (DACN) to comprehensively model feature interactions from different perspectives. Experiments on sufficient real-world data show that HOAN and DACN outperform state-of-the-art models.
TeamJUST at SemEval-2020 Task 4: Commonsense Validation and Explanation Using Ensembling Techniques

Common sense for natural language processing methods has been attracting a wide research interest, recently.Estimating automatically whether a sentence makes sense or not is considered an essential question.Task 4 in the International Workshop SemEval 2020 has provided three subtasks (A, B, and C) that challenges the participants to build systems for distinguishing the common sense statements from those that do not make sense.This paper describes TeamJUST's approach for participating in subtask A to differentiate between two sentences in English and classify them into two classes: common sense and uncommon sense statements.Our approach depends on ensembling four different state-of-the-art pre-trained models (BERT, ALBERT, Roberta, and XLNet).Our baseline model which we used only the pre-trained model of BERT has scored 89.1, while the TeamJUST model outperformed the baseline model with an accuracy score of 96.2.We have improved the results in the post-evaluation period to achieve our best result, which would rank the 4th in the competition if we had the chance to use our latest experiment.
TAG-it @ EVALITA2020: Overview of the Topic, Age, and Gender Prediction Task for Italian

The Gender Cross-Genre (GxG) task is a shared task on author profiling (in terms of gender) on Italian texts, with a specific focus on cross-genre performance.This task has been proposed for the first time at EVALITA 2018, providing different datasets from different textual genres: Twitter, YouTube, Children writing, Journalism, Personal diaries.Results from a total of 50 different runs show that the task is difficult to learn in itself: while almost all runs beat a 50% baseline, no model reaches an accuracy above 70%.We also observe that cross-genre modelling yields a drop in performance, but not as substantial as one would expect.