Reviewerly/exHarmony huggingface dataset:
RIJP at SemEval-2020 Task 1: Gaussian-based Embeddings for Semantic Change Detection

This paper describes the model proposed and submitted by our RIJP team to SemEval 2020 Task1: Unsupervised Lexical Semantic Change Detection.In the model, words are represented by Gaussian distributions.For Subtask 1, the model achieved average scores of 0.51 and 0.70 in the evaluation and post-evaluation processes, respectively.The higher score in the post-evaluation process than that in the evaluation process was achieved owing to appropriate parameter tuning.The results indicate that the proposed Gaussian-based embedding model is able to express semantic shifts while having a low computational complexity.
DUTH at SemEval-2020 Task 11: BERT with Entity Mapping for Propaganda Classification

This report describes the methods employed by the Democritus University of Thrace (DUTH) team for participating in SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles.Our team dealt with Subtask 2: Technique Classification.We used shallow Natural Language Processing (NLP) preprocessing techniques to reduce the noise in the dataset, feature selection methods, and common supervised machine learning algorithms.Our final model is based on using the BERT system with entity mapping.To improve our model's accuracy, we mapped certain words into five distinct categories by employing word-classes and entity recognition.
Active Selection Constraints for Semi-supervised Clustering Algorithms

Semi.-supervised clustering algorithms aim to enhance the performance of clustering using the pairwise constraints. However, selecting these constraints randomly or improperly can minimize the performance of clustering in certain situations and with different applications. In this paper, we select the most informative constraints to improve semi-supervised clustering algorithms. We present an active selection of constraints, including active must.-link (AML) and active cannot.-link (ACL) constraints. Based on Radial-Bases Function, we compute lower-bound and upper-bound between data points to select the constraints that improve the performance. We test the proposed algorithm with the base-line methods and show that our proposed active pairwise constraints outperform other algorithms.
Duluth at SemEval-2020 Task 12: Offensive Tweet Identification in English with Logistic Regression

This paper describes the Duluth systems that participated in SemEval-2020 Task 12, Multilingual Offensive Language Identification in Social Media (OffensEval-2020).We participated in the three English language tasks.Our systems provide a simple Machine Learning baseline using logistic regression.We trained our models on the distantly supervised training data made available by the task organizers and used no other resources.As might be expected we did not rank highly in the comparative evaluation: 79 th of 85 in Task A, 34 th of 43 in Task B, and 24 th of 39 in Task C. We carried out a qualitative analysis of our results and found that the class labels in the gold standard data are somewhat noisy.We hypothesize that the extremely high accuracy (> 90%) of the top ranked systems may reflect methods that learn the training data very well but may not generalize to the task of identifying offensive language in English.This analysis includes examples of tweets that despite being mildly redacted are still offensive.
The Impact of Strategic Leadership on Competitive Advantage: The Mediating Role of Ambidexterity and Information System: Evidence from Islamic Banks in Jordan

The purpose linked with the existing study is to examine the impact of strategic leadership on the competitive advantage of the Islamic banks in Jordan. The mediating analysis of organizational ambidexterity and information system among the links of strategic leadership on the competitive advantage of the Islamic banks in Jordan also included in the aim of the ongoing study. This study has executed the quantitative techniques of data collection through which data has been gathered from the employees of the Islamic banks by using questionnaires. The analysis part of the existing study has been accomplished by using the AMOS. The results indicated that the positive nexus has been found among the links of strategic leadership and competitive advantage while the findings also exposed that the ambidexterity positive mediates among the links of strategic leadership and competitive advantage of the Islamic banks in Jordan. These findings provided the guidelines to the regulators while formulating regulation for competitive advantage along with leadership and also helpful for the future studies who aim to investigate this area in the future.
SHA-256 in Parallel Blockchain Technology: Storing Land Related Documents

Everyday many land documents are stored in an offline process.This process is challenging because it is done manually; for registration and storing, it needs many papers too.However, there is also a lack of security because anyone can see the document.Safety problems can be solved by creating a secure digitized system and the successful implementation of the system.The digitalization of the land registry system through Block-chain could be the only solution to serve a helpful, more secure, and corruption-free solution.In this paper, a new block-chain architecture called parallel block-chain by Satellite Chain Formation algorithm specially designed to store land-related documents with the SHA-256 hash algorithm has been implemented.Polynomial equations also show the numbers of generated blocks and times.And this research also indicates that the proposed system is secure, scalable, and fast.
Electricity Load Forecasting with Collective Echo State Networks

Short-term load forecasting (STLF) is one of the most important tasks in power grid systems. Despite thorough research of STLF on community scales, predicting individual power consumption remains challenging. With the deployment of advanced metering infrastructure (AMI) technology, it is more convenient to access and operate load usage data on an individual scale. This work first investigates a novel deep learning method, Collective Long Short-term Memory (LSTM), which can fully utilize correlated meter information from AMI. Then, Collective Echo State Networks (ESN) are proposed as a simplified form of Collective LSTM using the reservoir method. Experimental results show that Collective ESN can achieve state-of-the-art prediction accuracy with extremely fast speed and fewer computational resources.
Svm Performance Optimization Using PSO for Breast Cancer Classification

Breast cancer is a very serious disease and requires the sufferer to undergo an intensive examination. This study proposes an optimization method for SVM parameters using the particle swarm optimization algorithm to classify breast cancer. The test results show that the classification using SVM with optimization using PSO is able to improve accuracy better than the classification using SVM without optimization, namely the determination of the parameters randomly. Breast cancer data classification accuracy increased to 78.91%. The best parameter values for c, γ, r, and d are 0.252101, 0.053248, 1 and 5, respectively. In this study, the polynomial and RBF kernels in general were able to produce higher accuracy than linear and sigmoid kernels.
STUDENTS’ BARRIERS TO ONLINE TUTORIAL

An online tutorial should be the core of the student learning support services in the digital era to maintain the quality of distanced higher education. However, in Universitas Terbuka with supposed to be the cyber university of Indonesia, the participation in the online tutorials was still not encouraging. For example, the average of the percentage of students’ involvement in the first academic semester of 2017 to the 2nd semester 2018 for one course was 2.21%. This study aimed to identify barriers for students to participate in the online tutorial. The method consisted of two stages. First, analyzing qualitative data that were gathered through open-ended questions to identify factors of the barriers. Second, developing 20 items’ questionnaire based on the factors identified in the first stage and analyzed using Partial Least Squares Structural Equation Model (PLS-SEM). The study involved 76 students in the first stage and 237 students in the second. The results of the study revealed that the students’ barriers to online tutorial reflected in four factors, i.e. (1) information, (2) motivation, (3) technical, and (4) supports; each factor confirmed significantly (p-values < 0.05) with t-values equal to 14.108, 27.875, 7.502, and 25.640, respectively. The study proposed some recommendations to improve student participation in online tutorials.
IITP-AINLPML at SemEval-2020 Task 12: Offensive Tweet Identification and Target Categorization in a Multitask Environment

In this paper, we describe the participation of IITP-AINLPML team in the SemEval-2020 Shared Task 12 on Offensive Language Identification and Target Categorization in English Twitter data.Our proposed model learns to extract textual features using a BiGRU-based deep neural network supported by a Hierarchical Attention architecture to focus on the most relevant areas in the text.We leverage the effectiveness of multitask learning while building our models for sub-task A and B. We do necessary undersampling of the over-represented classes in the sub-tasks A and C.During training, we consider a threshold of 0.5 as the separation margin between the instances belonging to classes OFF and NOT in sub-task A and UNT and TIN in sub-task B. For sub-task C, the class corresponding to the maximum score among the given confidence scores of the classes (IND, GRP and OTH) is considered as the final label for an instance.Our proposed model obtains the macro F1-scores of 90.95%, 55.69% and 63.88% in sub-task A, B and C, respectively.
Knowledge Transfer About Earthquake Disaster Mitigation To Children Through TF-IDF

Abstract: Past observations during a disaster identify that when children are separated from parents, they suffer due to the inability to comprehend disaster mitigation concepts. This study proposes a process from the existing framework K-Nearest Neighbor (KNN) and Term Frequency - Inverse Document Frequency (TF-IDF) for extracting a large body of knowledge in the form of documents into simple words. Those simple words can be arranged into contextual lyrics utilizing an Artificial Intelligence lyrics generator and then orchestrated into a song using a music generator. The piece, which is the output of the proposed process, is utilized to transfer the knowledge about earthquake disaster mitigation to children. A quantitative analysis of questionnaires on students aged 9-10 in Banda Aceh shows the song's highly significant effect in transferring the knowledge about earthquake disaster mitigation to children.
Taking the Correction Difficulty into Account in Grammatical Error Correction Evaluation

This paper presents performance measures for grammatical error correction which take into account the difficulty of error correction.To the best of our knowledge, no conventional measure has such functionality despite the fact that some errors are easy to correct and others are not.The main purpose of this work is to provide a way of determining the difficulty of error correction and to motivate researchers in the domain to attack such difficult errors.The performance measures are based on the simple idea that the more systems successfully correct an error, the easier it is considered to be.This paper presents a set of algorithms to implement this idea.It evaluates the performance measures quantitatively and qualitatively on a wide variety of corpora and systems, revealing that they agree with our intuition of correction difficulty.A scorer and difficulty weight data based on the algorithms have been made available on the web.
Anti-, Non-, and Dis-: the linguistics of negative meanings about youtube

The aim of this research paper is to reveal and explore the negative concepts, i.e. anti-values, verbalized in the professionally compiled media texts on the topic of YouTube as they shape the idea of what YouTube is and how it functions. The authors identify anti-values in the newspaper and magazine articles, reveal linguistic means through which they are implemented, as well as define their relationship with the context. The authors attempt to rationalize the use of anti-values in the texts about YouTube and provide a commentary on both linguistic and extra-linguistic features of these entities. Anti-values reflect concepts, phenomena, ideas or notions that are deemed unacceptable or undesirable in some cases universally, and in some specifically by the users of the platform or those affected by it. The research findings can be used in both axiological and socio-linguistic studies conducted for pragmatic, scientific or educational purposes.
Research on Cultivating Undergraduates in the Computer Science Based on Students

According to the existing problems in college students' learning, change the teaching method to cultivate students with innovative consciousness and innovative connotation.The article mainly elaborates on the current shortcomings in the teaching process of many ordinary colleges and universities, and proposes that teaching is mainly secondary school; teaching is supplemented, and student-oriented.The main responsibility of teachers is to guide patiently, actively communicate with students, and teach students in accordance with their aptitude.First teachers should understand the student's learning dynamics from the student's perspective, and then prescribe the right medicine to correct its drawbacks, encourage its advantages, and cultivate students' learning enthusiasm.Teachers stimulate students to learn actively, recognize their learning effects, and encourage students to experiment boldly and be creative.The students are more motivated to learn, build self-confidence in learning, and have a sense of innovation.According to the curriculum and being student-oriented, teachers prepare lessons, write lesson plans, design teaching links and do a good job in class counseling, which cultivate students' sense of innovation and enable students to have innovative connotations.
DETERMINATION OF BOOK LOAN ASSOCIATION PATTERN USING APRIORI ALGORITHM IN PUBLIC LIBRARIES

One way to increase knowledge is by reading literature from books. The young generation as the nation's hope generation is expected to have a high interest in reading to broaden their horizons. Libraries as a place for literature books to provide various types of books. The use of books available in the public libraries of Depok city is still insufficient as seen from the low number of books borrowed by members, especially among the younger generation aged 17 to 25 years. Therefore it is necessary to have a pattern to see the books that are often borrowed by members so that the supply of the types of books that are most attractive to the younger generation that is provided becomes more varied. The method used to determine the association pattern of lending books to the library is using a priori algorithm. From the research results, there are seven rule associations with books that have the highest confidence value and are most often borrowed simultaneously, namely history books and novels as well as history and philosophy. Based on rule association, it is able to assist library officers in providing the most popular types of books among adolescents. In addition, the research results can be used as a reference in placing the book layout on the shelf based on the books that are most often borrowed simultaneously.
Semi-Global Context Network for Semantic Correspondence

Estimating semantic correspondence between pairs of images can be challenging as a result of intra-class variation, background clutter, and repetitive patterns.This paper proposes a convolutional neural network (CNN) that attempts to learn rich semantic representations that contain the global semantic context to enable robust semantic correspondence estimation against intra-class variation and repetitive patterns.We introduce a global context fused feature representation that efficiently employs the global semantic context in estimating semantic correspondence as well as a semi-global self-similarity feature to reduce background clutter-induced distraction in capturing the global semantic context.The proposed network is trained in an end-to-end manner using a weakly supervised loss, which requires a weak level of supervision involving annotation on image pairs.This weakly supervised loss is supplemented with a historical averaging loss to effectively train the network.Our approach decreases running time by a factor of more than four and reduces the training memory requirement by a factor of three and produces competitive or superior results relative to previous approaches on the PF-PASCAL, PF-WILLOW, and TSS benchmarks.
Contrastive Analysis of English and Indonesian Idioms of Human Body

This research analyzes English Idioms which are equivalent in Indonesian Idioms especially idioms inrelation to human body. This research is conducted due to limited research on contrastive analysis in bothlanguages in the field of semantics. The method used in this research is descriptive qualitative method. Thedata in this research are idioms in English which have an equivalent in Indonesian idioms and vice versataken from dictionary of idioms and websites discussing idioms in both languages. Data are analyzed underthe framework of idiom classification by Fernando (1996) and types of contrastive analysis by Lado (1957).Based on the analysis that has been done, it can be concluded that mostly the equivalence of English andIndonesian idioms are in the form of pure idioms. In the level of contrastive analysis, the idioms arecategorized into three. The first is idioms in Indonesian and English which have similar meanings andsimilar forms of expressions. The second is idioms in Indonesian and English which have different meaningsbut similar forms of expressions. The third is idioms in Indonesian and English which have similarmeanings but different forms of expressions. The causes of similarities between the two idioms betweenEnglish and Indonesian is cultural factors between English and Indonesian in terms of percieving human body.Keywords: contrastive analysis, English Idiom, Indonesian Idiom, human body.
A Dynamic Prediction for Elastic Resource Allocation in Hybrid Cloud Environment

Cloud applications heavily use resources and generate more traffic specifically during specific events. In order to achieve quality in service provisioning, the elasticity of resources is a major requirement. With the use of a hybrid cloud model, organizations combine the private and public cloud services to deploy applications for the elasticity of resources. For elasticity, a traditional adaptive policy implements threshold-based auto-scaling approaches that are adaptive and simple to follow. However, during a high dynamic and unpredictable workload, such a static threshold policy may not be effective. An efficient auto-scaling technique that predicts the system load is highly necessary. Balancing a dynamism of load through the best auto-scale policy is still a challenging issue. In this paper, we suggest an algorithm using Deep learning and queuing theory concepts that proactively indicate an appropriate number of future computing resources for short term resource demand. Experiment results show that the proposed model predicts SLA violation with higher accuracy 5% than the baseline model. The suggested model enhances the elasticity of resources with performance metrics.
COVID-19 Response and Human Rights - Comments From the German and European Perspective

The coronavirus pandemic is a challenge for the constitutional state. The state must take measures to stop it but they have a serious impact on economic, social and private life and, thus, on human and fundamental rights. This contribution dicusses from the German and European perspective the classical human and fundamental rights questions raised by the COVID-19 restrictions but also the significance of the second dimension of these rights, the state's duties of protection (or positive obligations). Finally, it focuses on the decisive problems of the proportionality of the COVID-19 restrictions.
Stimulating Primary School Children's Creativity

Stimulating primary school children’s creativity is appointed as one of the essential keys to the educational system 's progress. The main purpose of the paper arrives from the necessity for different fields of the current society to stimulate primary school children's creativity, who are bound to lead to original and creative solutions. This subject highlights the importance of stimulating primary school children’s creativity in the context of current education. The approach in the debate deals with the link between creative thinking and creative problem solving alongside the importance of stimulating primary school children's creativity. A great experience for primary school education development includes creative thinking and creative problem solving. Creativity involves the process of divergent thinking that includes the beginning of wonderful ideas, creating new connections, expanding the limits of knowledge and split of ideas, which are old. When the children's divergent thinking is boosted, it enormously helps to maintain children's motivation for extended learning.. In the same way, encouraging primary school children to keep on generating original and new ideas fosters their creative-thinking abilities. Personal characteristics of a child can influence a certain grade in stimulating the creativity. Children develop quite complex skills when they begin to be familiar with problem solving skills and develop creative thinking. Therefore, their creative characteristics should be developed and in the same way, provide creative learning opportunities and inspiring, original lessons. A variety of studies that contained creativity included stimulating primary school children’s creativity. This aspect represents global educational progress to society and the current study provides a better view of creativity.
PUM at SemEval-2020 Task 12: Aggregation of Transformer-based Models’ Features for Offensive Language Recognition

In this paper, we describe the PUM team's entry to the SemEval-2020 Task 12. Creating our solution involved leveraging two well-known pretrained models used in natural language processing: BERT and XLNet, which achieve state-of-the-art results in multiple NLP tasks.The models were fine-tuned for each subtask separately and features taken from their hidden layers were combined and fed into a fully connected neural network.The model using aggregated Transformer features can serve as a powerful tool for offensive language identification problem.Our team was ranked 7th out of 40 in Sub-task C -Offense target identification with 64.727% macro F1-score and 64th out of 85 in Sub-task A -Offensive language identification (89.726%F1-score).
Fine-grained Information Status Classification Using Discourse Context-Aware BERT

Previous work on bridging anaphora recognition (Hou et al., 2013a) casts the problem as a subtask of learning fine-grained information status (IS).However, these systems heavily depend on many hand-crafted linguistic features.In this paper, we propose a simple discourse context-aware BERT model for fine-grained IS classification.On the ISNotes corpus (Markert et al., 2012), our model achieves new state-of-the-art performance on fine-grained IS classification, obtaining a 4.8 absolute overall accuracy improvement compared to Hou et al. (2013a).More importantly, we also show an improvement of 10.5 F1 points for bridging anaphora recognition without using any complex hand-crafted semantic features designed for capturing the bridging phenomenon.We further analyze the trained model and find that the most attended signals for each IS category correspond well to linguistic notions of information status.