Reviewerly/exHarmony huggingface dataset:
SO at SemEval-2020 Task 7: DeepPavlov Logistic Regression with BERT Embeddings vs SVR at Funniness Evaluation

This paper describes my efforts in evaluating how editing news headlines can make them funnier within the frames of SemEval 2020 Task 7. I participated in both of the sub-tasks: Sub-Task 1 "Regression" and Sub-task 2 "Predict the funnier of the two edited versions of an original headline".I experimented with a number of different models, but ended up using DeepPavlov logistic regression (LR) with BERT English cased embeddings for the first sub-task and support vector regression model (SVR) for the second.RMSE score obtained for the first task was 0.65099 and accuracy for the second -0.32915.
Long Term Evolution Anomaly Detection and Root Cause Analysis for Data Throughput Optimization

There is a growing demand for mobile network data services which is driven by high number of smartphones and web content search as multimedia.Network operators have tried to provide enough capacity and meet the data speeds that the customer needs.This has led to introduction of new technology and expansion of the mobile networks making them complex to manage.Detecting anomalies that affect data throughput and investigating the root causes in mobile networks is challenging as mobile environments are increasingly complex, heterogeneous, and evolving.There is need to automate network management activities to improve network management processes and prevent revenue loss.Self-Organizing network is a standard introduced by third Generation Partnership Program (3GPP) to automate network management.However, the standard is still not fully developed.This project focused on implementing an anomaly detection and root cause analysis model that helps in the process of data throughput optimization in Long-term evolution (LTE) networks.The model used Density Based Spatial Clustering of Applications with Noise (DBSCAN) for anomaly detection, K-Nearest Neighbour (KNN) for root cause analysis and real network performance data from a Kenyan operator.Proposed anomaly detection model achieved a silhouette coefficient of 0.451 showing a good separation of existing clusters in the dataset and was able to detect anomalies with both positive and negative impact on data throughput.The root cause analysis model achieved an accuracy of 94.59% and was able to identify the root cause of detected anomalies that had a negative impact on data throughput.
Evaluation of Rapidminer-Aplication in Data Mining Learning using PeRSIVA Model

RapidMiner is an application that is used to analyze data quantities and qualitatively to obtain information and knowledge as expected. This software is implemented to process data using several methods or algorithms in Data Minig learning. However, when using this software, users sometimes cannot distinguish between various methods or algorithms in Data Mining. Therefore, it is necessary to evaluate to optimize the use of this software in data mining learning. This study focuses on RapidMiner evaluation of data mining learning using the Persiva model. This model consists of aspects of satisfaction, behavior, impact, and effectiveness. The data collection technique was in the form of a questionnaire with 48 subjects. Data analysis used is descriptive statistics to determine satisfaction, behavior and effects. Meanwhile, Think-Aloud Retrospective technique is used to determine the effectiveness of RapidMiner. Our findings show that users are satisfied with the results of respondents on average agreeing (80%), in the aspect of behavior and impact, the percentage results are above 80%, and the use of this application has been effective with a completion rate above 90%. So it can be concluded that by using this application in data mining learning users can easily complete tasks, and be motivated, and add insights and knowledge in relevant disciplines.
QUALIMETRIC APPROACH TO EVALUATING DIGITAL COMPETENCIES OF TEACHERS

The article is devoted to the study of the problems of the formation and evaluation of digital competencies in the context of digitalization of general education. Currently, the problem of determining the level of formation of competencies among specialists in any field is an urgent task in the preparation and competitive selection of specialists. In the Russian education system, the training of future teachers and advanced training, additional professional education of experienced teachers should go to a new level of development in connection with the implementation of competency-based educational programs. And in these conditions, it becomes necessary to search for new approaches to assessing professional and digital competencies in general education teachers. Various models and algorithms for the formation of professional and digital competencies in general education teachers are studied. The approaches to the development of a cognitive model of competency formation are identified. A qualimetric approach to assessing digital competencies is revealed, as one of the most effective. Descriptors of key competencies of the digital economy, digital competencies are developed. The criteria for assessing digital and professional competencies formed by general education teachers were identified, the main indicators for evaluating the results were determined. A mechanism and means for assessing the level of formation of digital competencies, a scale for translating assessments of the level of competencies into a verbal assessment system have been developed. Based on the main indicators for assessing results, competency matrices have been developed for all stages of competency formation. An appraisal fund has been developed, consisting of various competency-level appraisals that have both formative and appraisal functions. This study allows us to develop a cognitive model of the formation of the digital competence of a teacher in the context of digitalization of general education. The results of the study can become the basis for the modernization of the system of training and retraining of teachers in the context of digitalization of general education.
Interpretable NLG for Task-oriented Dialogue Systems with Heterogeneous Rendering Machines

End-to-end neural networks have achieved promising performances in natural language generation (NLG). However, they are treated as black boxes and lack interpretability. To address this problem, we propose a novel framework, heterogeneous rendering machines (HRM), that interprets how neural generators render an input dialogue act (DA) into an utterance. HRM consists of a renderer set and a mode switcher. The renderer set contains multiple decoders that vary in both structure and functionality. For every generation step, the mode switcher selects an appropriate decoder from the renderer set to generate an item (a word or a phrase). To verify the effectiveness of our method, we have conducted extensive experiments on 5 benchmark datasets. In terms of automatic metrics (e.g., BLEU), our model is competitive with the current state-of-the-art method. The qualitative analysis shows that our model can interpret the rendering process of neural generators well. Human evaluation also confirms the interpretability of our proposed approach.
JUSTMasters at SemEval-2020 Task 3: Multilingual Deep Learning Model to Predict the Effect of Context in Word Similarity

There is a growing research interest in studying word similarity.Without a doubt, two similar words in a context may be considered different in another context.Therefore, this paper investigates the effect of the context in word similarity.The SemEval-2020 workshop has provided a shared task (Task 3: Predicting the (Graded) Effect of Context in Word Similarity).In this task, the organizers provided unlabeled datasets for four languages, English, Croatian, Finnish, and Slovenian.Our team, JUSTMasters, has participated in this competition in the two subtasks: A and B. Our approach has used a weighted average ensembling method for different pre-trained embeddings techniques for each of the four languages.Our proposed model outperformed the baseline models in both subtasks and achieved the best result for subtask 2 in English and Finnish, with a score of 0.725 and 0.68, respectively.We have been ranked the sixth in subtask 1, with English, Croatian, Finnish, and Slovenian with scores as follows: 0.738, 0.44, 0.546, 0.512.
Pin_cod_ at SemEval-2020 Task 12: Injecting Lexicons into Bidirectional Long Short-Term Memory Networks to Detect Turkish Offensive Tweets

This paper describes a system (pin_cod_) built for SemEval 2020 Task 12: OffensEval: Multilingual Offensive Language Identification in Social Media (Zampieri et al., 2020). I present the system based on the architecture of bidirectional long short-term memory networks (BiLSTM) concatenated with lexicon-based features and a social-network specific feature and then followed by two fully connected dense layers for detecting Turkish offensive tweets. The pin cod ’s system achieved a macro F1-score of 0.7496 for Sub-task A - Offensive language identification in Turkish.
Praktik signifikasi atas transformasi pelayanan referensi di Perpustakaan Universitas Indonesia

The transformation of reference services in academic libraries in the era of information technology has a significant impact on librarians. Librarians need to address transformation properly to design the right services. The University of Indonesia (UI) library has undergone this transformation since a decade ago and continues to adapt to existing developments. Various service concepts were developed to suit users' demands, including by providing online-based services and utilizing social media to communicate with users. This study aimed to explore how reference librarians interpreted this transformation and what they should do. The research method used qualitative methods with the phenomenological method narrated descriptively. The research informants consisted of 13 librarians and seven reference librarians. Data analysis showed that librarians'significance for transforming reference services resulted in 4 important themes that were interpreted as influencing their work as reference librarians. The four themes were digital-based library services, expansion of access, policy, and promotion. This research concludes that users and librarians' practice of significance for the transformation of reference services results in the digital libraries' concept, which requires expansion of access, relevant policies, and social media optimization for service promotion. The practice of significance related to communication patterns with reference users in the digital era effectively uses email and chatting on the WhatsApp application.
AprilE: Attention with Pseudo Residual Connection for Knowledge Graph Embedding

Knowledge graph embedding maps entities and relations into low-dimensional vector space.However, it is still challenging for many existing methods to model diverse relational patterns, especially symmetric and antisymmetric relations.To address this issue, we propose a novel model, AprilE, which employs triple-level self-attention and pseudo residual connection to model relational patterns.The triple-level self-attention treats head entity, relation, and tail entity as a sequence and captures the dependency within a triple.At the same time the pseudo residual connection retains primitive semantic features.Furthermore, to deal with symmetric and antisymmetric relations, two schemas of score function are designed via a position-adaptive mechanism.Experimental results on public datasets demonstrate that our model can produce expressive knowledge embedding and significantly outperforms most of the state-of-the-art works.
UIC-NLP at SemEval-2020 Task 10: Exploring an Alternate Perspective on Evaluation

In this work we describe and analyze a supervised learning system for word emphasis selection in phrases drawn from visual media as a part of the Semeval 2020 Shared Task 10.More specifically, we begin by briefly introducing the shared task problem and provide an analysis of interesting and relevant features present in the training dataset.We then introduce our LSTM-based model and describe its structure, input features, and limitations.Our model ultimately failed to beat the benchmark score, achieving an average match() score of 0.704 on the validation data (0.659 on the test data) but predicted 84.8% of words correctly considering a 0.5 threshold.We conclude with a thorough analysis and discussion of erroneous predictions with many examples and visualizations.
The SADID Evaluation Datasets for Low-Resource Spoken Language Machine Translation of Arabic Dialects

Low-resource Machine Translation recently gained a lot of popularity, and for certain languages, it has made great strides.However, it is still difficult to track progress in other languages for which there is no publicly available evaluation data.In this paper, we introduce benchmark datasets for Arabic and its dialects.We describe our design process and motivations and analyze the datasets to understand their resulting properties.Numerous successful attempts use large monolingual corpora to augment low-resource pairs.We try to approach augmentation differently and investigate whether it is possible to improve MT models without any external sources of data.We accomplish this by bootstrapping existing parallel sentences and complement this with multilingual training to achieve strong baselines.
DESIGN AND SIMULATION ANALYSIS OF 1 MWP GRID-CONNECTED PHOTOVOLTAIC SYSTEM AT GAZIANTEP AIRPORT, TURKEY

The use of photovoltaic power plants is gradually increasing in order to reduce energy costs and greenhouse gas emissions at airports. Airports are suitable settlements for the installation of photovoltaic power plants as they have vast and free of shade areas that are not used in aviation activities. In this study, a 1 MWp photovoltaic power plant is proposed for Gaziantep Airport, Turkey. Performance, economic and environmental benefits of the proposed system were analyzed using the PVsyst simulation tool developed by the University of Geneva in Switzerland. The study demonstrates that Gaziantep Airport is suitable to installation of a grid-connected photovoltaic system and has a high solar energy resource. The proposed photovoltaic power plant at Gaziantep Airport is predicted to operate with an annual electricity generation of 1702.09 MWh, 78.6 % annual average performance ratio (PR), 19.43 % average capacity factor (CF) and 4.67 [h/d] annual average daily final yield.
NLP_UIOWA at SemEval-2020 Task 8: You’re Not the Only One Cursed with Knowledge - Multi Branch Model Memotion Analysis

We propose hybrid models (HybridE and HybridW) for meme analysis (SemEval 2020 Task 8), which involves sentiment classification (Subtask A), humor classification (Subtask B), and scale of semantic classes (Subtask C).The hybrid model consists of BLSTM and CNN for text and image processing respectively.HybridE provides equal weight to BLSTM and CNN performance, while HybridW provides weightage based on the performance of BLSTM and CNN on a validation set.The performances (macro F1) of our hybrid model on Subtask A are 0.329 (HybridE), 0.328 (HybridW), on Subtask B are 0.507 (HybridE), 0.512 (HybridW), and on Subtask C are 0.309 (HybridE), 0.311 (HybridW).
Socially Aware Design of Games: an early workshop for game designers

Designing games to encourage players' positive and healthy behaviors is both a necessity and a challenge. Although the game research field has relevant contributions in literature, especially regarding gamification, serious games and persuasive computing, when the application domain is critical, such as education or health, it is necessary to understand the context in which a game will be inserted before actually proposing and designing it. This paper presents a collaborative workshop for problem understanding and requirements elicitation in the early stages of game design. The workshop stresses the importance of problem-understanding from a sociotechnical perspective before developing a game and before directly engaging other stakeholders. The paper presents the workshop in practice where designers must understand how to support children to perform speech therapy exercises outside hospital settings. The workshop was conducted to produce a systemic understanding of the problem domain, of human and technical aspects, possible solutions and their implications to be used as inputs for game design and evaluation. The paper presents the workshop and its main results, discussing lessons learned and highlighting the need for early and lean practices to promote the socially aware design of games.
Towards Instance-Level Parser Selection for Cross-Lingual Transfer of Dependency Parsers

Current methods of cross-lingual parser transfer focus on predicting the best parser for a lowresource target language globally, that is, "at treebank level".In this work, we propose and argue for a novel cross-lingual transfer paradigm: instance-level parser selection (ILPS), and present a proof-of-concept study focused on instance-level selection in the framework of delexicalized parser transfer.Our work is motivated by an empirical observation that different source parsers are the best choice for different Universal POS-sequences (i.e., UPOS sentences) in the target language.We then propose to predict the best parser at the instance level.To this end, we train a supervised regression model, based on the Transformer architecture, to predict parser accuracies for individual POS-sequences.We compare ILPS against two strong single-best parser selection baselines (SBPS): (1) a model that compares POS n-gram distributions between the source and target languages (KL) and ( 2) a model that selects the source based on the similarity between manually created language vectors encoding syntactic properties of languages (L2V).The results from our extensive evaluation, coupling 42 source parsers and 20 diverse low-resource test languages, show that ILPS outperforms KL and L2V on 13/20 and 14/20 test languages, respectively.Further, we show that by predicting the best parser "at treebank level" (SBPS), using the aggregation of predictions from our instance-level model, we outperform the same baselines on 17/20 and 16/20 test languages.
Normalizing Compositional Structures Across Graphbanks

The emergence of a variety of graph-based meaning representations (MRs) has sparked an important conversation about how to adequately represent semantic structure.MRs exhibit structural differences that reflect different theoretical and design considerations, presenting challenges to uniform linguistic analysis and cross-framework semantic parsing.Here, we ask the question of which design differences between MRs are meaningful and semantically-rooted, and which are superficial.We present a methodology for normalizing discrepancies between MRs at the compositional level (Lindemann et al., 2019), finding that we can normalize the majority of divergent phenomena using linguistically-grounded rules.Our work significantly increases the match in compositional structure between MRs and improves multi-task learning (MTL) in a low-resource setting, serving as a proof of concept for future broad-scale cross-MR normalization.
LAST at SemEval-2020 Task 10: Finding Tokens to Emphasise in Short Written Texts with Precomputed Embedding Models and LightGBM

To select tokens to be emphasised in short texts, a system mainly based on precomputed embedding models, such as BERT and ELMo, and LightGBM is proposed.Its performance is low.Additional analyzes suggest that its effectiveness is poor at predicting the highest emphasis scores while they are the most important for the challenge and that it is very sensitive to the specific instances provided during learning.
Application Of The Gamification Principles In Vocational Guidance Of Pupils

The relevance of the research of this problem is due to the need to develop new methods of vocational guidance of modern pupils.The key issue of the research is the development of a mobile app in the digital platform system as the basis of an early vocational guidance model.This model is based on the principles of gamification.The purpose of our research is to justify and describe a mobile app on a device running the Android and iOS operating systems.The research methods included analysis of scientific publications on the problem; the analysis of experience of vocational guidance of educational organizations; the method of modeling a mobile application for early vocational guidance of schoolchildren based on the principles of gamification.The mobile app is one of the tools of the digital platform.It allows you to capture the digital footprint of each participant which is formed in the process and result of the developing vocational guidance.The mobile app is a set of tools that allow pupils to record the number of classes attended for each type of profession: human-human, human-nature, human-technology, humansign system, human-artistic image.It also allows to track and record a qualitative indicator of the child's involvement in a particular area according to standardized assessment methods.As a result of using the mobile app, a digital footprint is formed as statistics on the areas of classes and activities that the child chose.
Comparative Probing of Lexical Semantics Theories for Cognitive Plausibility and Technological Usefulness

Lexical semantics theories differ in advocating that the meaning of words is represented as an inference graph, a feature mapping or a cooccurrence vector, thus raising the question: is it the case that one of these approaches is superior to the others in representing lexical semantics appropriately? Or in its non antagonistic counterpart: could there be a unified account of lexical semantics where these approaches seamlessly emerge as (partial) renderings of (different) aspects of a core semantic knowledge base? In this paper, we contribute to these research questions with a number of experiments that systematically probe different lexical semantics theories for their levels of cognitive plausibility and of technological usefulness. The empirical findings obtained from these experiments advance our insight on lexical semantics as the feature-based approach emerges as superior to the other ones, and arguably also move us closer to finding answers to the research questions above.
Ferryman as SemEval-2020 Task 5: Optimized BERT for Detecting Counterfactuals

The main purpose of this article is to state the effect of using different methods and models for counterfactual determination and detection of causal knowledge.Nowadays, counterfactual reasoning has been widely used in various fields.In the realm of natural language process(NLP), counterfactual reasoning has huge potential to improve the correctness of a sentence.In the shared Task 5 of detecting counterfactual in SemEval 2020, we pre-process the officially given dataset according to case conversion, extract stem and abbreviation replacement.We use last-5 bidirectional encoder representation from bidirectional encoder representation from transformer (BERT)and term frequency-inverse document frequency (TF-IDF) vectorizer for counterfactual detection.Meanwhile, multi-sample dropout and cross validation are used to improve versatility and prevent problems such as poor generosity caused by overfitting.Finally, our team Ferryman ranked the 8th place in the sub-task 1 of this competition.