Reviewerly/exHarmony huggingface dataset:
Toys for Conviviality. Situating Commoning, Computation and Modelling

Abstract This article explores the use of agent-based modelling as a critical and playful form of engagement with cooperative housing organizations. Because of its inherent complexities vis-à-vis decision-making, commoning is a well-suited field of study to explore the potential of humanities-driven experimental design (media) research to provoke critical reflection, problem-finding and productive complication. By introducing two different agent-based models, the interdisciplinary research team discusses their experience with setting up parameters for modelling, their implications, and the possibilities and limits of employing modelling techniques as a basis for decision-making. While it shows that modelling can be helpful in detecting long-term results of decisions or testing out effects of unlikely yet challenging events, modelling might act as a discursive practice uncovering hidden assumptions inherent in the model setup and generating an increase of scientific uncertainty. The project “ThinkingToys for Commoning” thus argues for a critical modelling practice and culture, in which models act as toys for probing alternative modes of living together and exploring the constructedness of methods. In countering late forms of capitalism, the resulting situated and critical practice provides avenues for enabling more self-determined forms of governance.
BhamNLP at SemEval-2020 Task 12: An Ensemble of Different Word Embeddings and Emotion Transfer Learning for Arabic Offensive Language Identification in Social Media

Social media platforms such as Twitter offer people an opportunity to publish short posts in which they can share their opinions and perspectives.While these applications can be valuable, they can also be exploited to promote negative opinions, insults, and hatred against a person, race, or group.These opinions can be spread to millions of people at the click of a mouse.As such, there is a need to develop mechanisms by which offensive language can be automatically detected in social media channels and managed in a timely manner.To help achieve this goal, SemEval 2020 offered a shared task (OffensEval 2020) that involved the detection of offensive text in Arabic.We propose an ensemble approach that combines different levels of word embedding models and transfer learning from other sources of emotion-related tasks.The proposed system ranked 9th out of the 52 entries within the Arabic Offensive language identification subtask.
A Dialog Manager for Micro-Worlds

The paper describes the micro-world-based dialog manager which was developed in the ROBIN project.The manager was designed to be loaded into the Pepper robot, used in real-world scenarios and interface with real-time automatic speech recognition and synthesis for Romanian language.A strict requirement for the development of the dialog manager was that it had to be configurable, with minimum user intervention, to a wide range of dialogue situations, such as assisting the elderly in day to day routine tasks or providing orientation in a building for new visitors.Thus, the dialog manager was programmed to configure itself from micro-world specification files containing definitions of the concepts one can speak about and definitions of the first-order predicates that are true in the micro-world.
Placement for Intercommunicating Virtual Machines in Autoscaling Cloud Infrastructure

Due to pay-as-you-go style adopted by cloud datacenters (DC), modern day applications having intercommunicating tasks depend on DC for their computing power. Due to unpredictability of rate at which data arrives for immediate processing, application performance depends on autoscaling service of DC. Normal VM placement schemes place these tasks arbitrarily onto different physical machines (PM) leading to unwanted network traffic resulting in poor application performance and increases the DC operating cost. This paper formulates autoscaling and intercommunication aware task placements (AIATP) as an optimization problem, with additional constraints and proposes solution, which uses the placement knowledge of prior tasks of individual applications. When compared with well-known algorithms, CloudsimPlus-based simulation demonstrates that AIATP reduces the resource fragmentation (30%) and increases the resource utilization (18%) leading to minimal number of active PMs. AIATP places 90% tasks of an application together and thus reduces the number of VM migration (39%) while balancing the PMs.
The Application and Research of College English Blended Teaching Mode Based on MosoTeach

Now it is the digital information age. With the maturity of mobile Internet technology and the increase of online education demand, mobile learning applications based on mobile Internet terminals have been vigorously developed and widely used. Blended English teaching based on mobile network technology has become an inevitable trend in College English teaching. The new teaching mode is student-centered and emphasizes the interaction between teachers and students. The implementation of personalized teaching mode is conducive to improving students' learning autonomy and innovation, it can improve the classroom teaching effect to a certain extent. At present, the post-90s college students are the pioneers of the Internet era, but also the trendsetters in the wave of mobile Internet. How to effectively apply the mobile learning platform such as MosoTeach to College English teaching is the core theme of this paper.
Genre Classification of Telugu and English Movie Based on the Hierarchical Attention Neural Network

Classification of movies is useful in the movie recommendation system for video streaming applications like Amazon, Netflix, etc.The existing methods used either video or audio data as input that requires more computation resources to process the data for the genre classification of movies.In this study, the Hierarchical Attention Neural Network (HANN) is proposed for genre classification of movies based on the social media called Twitter data as input.Twitter data related to the Telugu and English movies are collected and applied to HANN for movie's genre classification.IMDB data are used to evaluate the performance of the proposed HANN method.The hierarchical structures of the twitter data is considered by the proposed HANN method and the most important words related to genre classification is identified by the attention mechanism, where the other neural networks such as Artificial Neural Network and Convolutional Neural Network (CNN) returns only the important weights resulting from previous words.The HANN method has the advantages of encoding the relevant information that helps to improve the performance of the recommendation system.The experimental results show that the HANN method achieve higher performance compared to other classifiers Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM).The HANN method achieves accuracy of 73.15% in classification, while the existing BiLSTM method achieve the accuracy of 68% in classification.
On Investigating Both Effectiveness and Efficiency of Embedding Methods in Task of Similarity Computation of Nodes in Graphs

One of the important tasks in a graph is to compute the similarity between two nodes; link-based similarity measures (in short, similarity measures) are well-known and conventional techniques for this task that exploit the relations between nodes (i.e., links) in the graph. Graph embedding methods (in short, embedding methods) convert nodes in a graph into vectors in a low-dimensional space by preserving social relations among nodes in the original graph. Instead of applying a similarity measure to the graph to compute the similarity between nodes a and b, we can consider the proximity between corresponding vectors of a and b obtained by an embedding method as the similarity between a and b. Although embedding methods have been analyzed in a wide range of machine learning tasks such as link prediction and node classification, they are not investigated in terms of similarity computation of nodes. In this paper, we investigate both effectiveness and efficiency of embedding methods in the task of similarity computation of nodes by comparing them with those of similarity measures. To the best of our knowledge, this is the first work that examines the application of embedding methods in this special task. Based on the results of our extensive experiments with five well-known and publicly available datasets, we found the following observations for embedding methods: (1) with all datasets, they show less effectiveness than similarity measures except for one dataset, (2) they underperform similarity measures with all datasets in terms of efficiency except for one dataset, (3) they have more parameters than similarity measures, thereby leading to a time-consuming parameter tuning process, (4) increasing the number of dimensions does not necessarily improve their effectiveness in computing the similarity of nodes.
JBNU at SemEval-2020 Task 4: BERT and UniLM for Commonsense Validation and Explanation

This paper presents our contributions to the SemEval-2020 Task 4 Commonsense Validation and Explanation (ComVE) and includes the experimental results of the two Subtasks B and C of the SemEval-2020 Task 4. Our systems rely on pre-trained language models, i.e., BERT (including its variants) and UniLM, and rank 10th and 7th among 27 and 17 systems on Subtasks B and C, respectively.We analyze the commonsense ability of the existing pretrained language models by testing them on the SemEval-2020 Task 4 ComVE dataset, specifically for Subtasks B and C, the explanation subtasks with multi-choice and sentence generation, respectively.
UPB at SemEval-2020 Task 9: Identifying Sentiment in Code-Mixed Social Media Texts Using Transformers and Multi-Task Learning

Sentiment analysis is a process widely used in opinion mining campaigns conducted today.This phenomenon presents applications in a variety of fields, especially in collecting information related to the attitude or satisfaction of users concerning a particular subject.However, the task of managing such a process becomes noticeably more difficult when it is applied in cultures that tend to combine two languages in order to express ideas and thoughts.By interleaving words from two languages, the user can express with ease, but at the cost of making the text far less intelligible for those who are not familiar with this technique, but also for standard opinion mining algorithms.In this paper, we describe the systems developed by our team for SemEval-2020 Task 9 that aims to cover two well-known code-mixed languages: Hindi-English and Spanish-English.We intend to solve this issue by introducing a solution that takes advantage of several neural network approaches, as well as pre-trained word embeddings.Our approach (multlingual BERT) achieves promising performance on the Hindi-English task, with an average F1-score of 0.6850, registered on the competition leaderboard, ranking our team 16 th out of 62 participants.For the Spanish-English task, we obtained an average F1-score of 0.
Don’t Invite BERT to Drink a Bottle: Modeling the Interpretation of Metonymies Using BERT and Distributional Representations

In this work, we carry out two experiments in order to assess the ability of BERT to capture the meaning shift associated with metonymic expressions.We test the model on a new dataset that is representative of the most common types of metonymy.We compare BERT with the Structured Distributional Model (SDM), a model for the representation of words in context which is based on the notion of Generalized Event Knowledge.The results reveal that, while BERT ability to deal with metonymy is quite limited, SDM is good at predicting the meaning of metonymic expressions, providing support for an account of metonymy based on event knowledge.
Gender Identification Over Voice Sample Using Machine Learning

Point of this paper is to style a gender reorganization system that distinguishes the gender of the agent. Gender classification is associate rising space of analysis for the accomplishment of economical interaction between human and machine victimization speech files. Numerous ways in which are planned for the gender classification within the past. Speech recognition is a chief approach for the identification of the supply. In this work different feature for the gender classification are gait of person, lips shape, automatic face recognition, iris code, etc. There aretwo experiments performed, the 1st experiment deals with the gender classification of ten completely different languages, every language consists of fifteen audio files and also the best accuracy achieved by the machine learning technique that's straight forward logistical (87.33%). The second experiment deals with the formation of language identification system within which Russian and Korean has the simplest combination of language with the a hundred accuracy by multilayer perceptron.
Name Disambiguation Analysis Using the Word Sense Disambiguation Method in Hadith

Name disambiguation is the problem solving process to find similar names in sentences. The ambiguity of names can be found in hadith of Sahih Bukhari, names "Abdullah bin Amru" in hadiths no 27 and “Abdullah bin Amru” in hadith no 58, These names are the same, but there is no proof they are the same person. This problem is the early indication of ambiguity of name in the hadith. Based in this problem, this research aims to find name disambiguation of hadith narrators with classification by considering the perawi chain. To solved this problem the authors used Word Sense Disambiguation (WSD), WSD is a process to assign the same meaning from the sentences, based on the context in which the word appears. To classify several names in the hadith, the authors used KNN algorithm, by combining the WSD and KNN method can reduce the ambiguity of names in hadith. The data used in this study came from the hadith of Sahih Bukhori through the pre-processing stage. After conducting the research showed a collection of hadith numbers with the same name prediction with an accuracy of 99% at k = 1. Thus, this method can be used for name disambiguation.
Attacking Trivium at the Bitstream Level

In this paper, we present a bitstream modification attack on the Trivium stream cipher, an international standard under ISO/IEC 29192-3. By changing the content of three LUTs in the bitstream, we reduce the non-linear state updating function of Trivium to a linear one. This makes it possible to recover the key from 288 keystream bits using at most 2 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">19.41</sub> operations. We also propose a countermeasure against bitstream modification attacks which obfuscates the bitstream using dummy and camouflaged LUTs which look legitimate to the attacker. We present an algorithm for injecting dummy LUTs directly into the bitstream without causing any performance or power penalty.
Analyses of Character Emotions in Dramatic Works by Using EmoLex Unigrams

In theatrical pieces, written language is the primary medium for establishing antagonisms. As one of the most important figures of renaissance, Shakespeare wrote characters which express themselves clearly. Thus, the emotional landscape of the plays can be revealed from the texts. It is important to analyze such landscapes for further demonstrating these structures.We use word-emotion association lexicon with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and two sentiments (negative and positive). By using this lexicon, the emotional state of each character is represented in 10 dimensional space and mapped onto a plane. This principle axes planes position each character relatively. Additionally, tempora-emotional evaluation of each play is graphed.We conclude that the protagonist and the antagonist have different emotional states from the rest and these two emotionally oppose each other. Temporal-Emotional timeline of the plays are meaningful to have a better insight into the tragedies.
INFLUENTIAL FACTORS IN DESIGN AND IMPLEMENTATION OF VIRTUAL REALITY TECHNOLOGY

Virtual Reality (VR) is a computer-generated environment that creates a simulated experience for users. VR technology is used as an effective training tool, especially for teaching potentially dangerous and/or difficult-to-create scenarios in real-life. Despite the vast literature that shows the positive impact of VR training on the learners’ performance and experience, studies that investigate the criteria for the selection of appropriate VR technology are quite limited. This paper proposes key decision criteria in selecting and investing in the right VR technology to create the best possible learning outcome based on the feedback from VR developers and engineers in the US. Ease of installation and creating an immersive user experience are identified as the most influential factors affecting the successful implementation of VR technology. We envision that this study will help companies that plan to invest in VR technologies better understand the critical factors affecting the successful design and implementation of the technology and hence will aid in decision making.
Towards Pedestrian Target Detection with Optimized Mask R-CNN

Aiming at the problem of low pedestrian target detection accuracy, we propose a detection algorithm based on optimized Mask R-CNN which uses the latest research results of deep learning to improve the accuracy and speed of detection results. Due to the influence of illumination, posture, background, and other factors on the human target in the natural scene image, the complexity of target information is high. SKNet is used to replace the part of the convolution module in the depth residual network model in order to extract features better so that the model can adaptively select the best convolution kernel during training. In addition, according to the statistical law, the length-width ratio of the anchor box is modified to make it more accord with the natural characteristics of the pedestrian target. Finally, a pedestrian target dataset is established by selecting suitable pedestrian images in the COCO dataset and expanded by adding noise and median filtering. The optimized algorithm is compared with the original algorithm and several other mainstream target detection algorithms on the dataset; the experimental results show that the detection accuracy and detection speed of the optimized algorithm are improved, and its detection accuracy is better than other mainstream target detection algorithms.
MedWriter: Knowledge-Aware Medical Text Generation

To exploit the domain knowledge to guarantee the correctness of generated text has been a hot topic in recent years, especially for high professional domains such as medical.However, most of recent works only consider the information of unstructured text rather than structured information of the knowledge graph.In this paper, we focus on the medical topic-to-text generation task and adapt a knowledge-aware text generation model to the medical domain, named MedWriter, which not only introduces the specific knowledge from the external MKG but also is capable of learning graph-level representation.We conduct experiments on a medical literature dataset collected from medical journals, each of which has a set of topic words, an abstract of medical literature and a corresponding knowledge graph from CMeKG.Experimental results demonstrate incorporating knowledge graph into generation model can improve the quality of the generated text and has robust superiority over the competitor methods.
Endangered Languages meet Modern NLP

This tutorial will focus on NLP for endangered languages documentation and revitalization. First, we will acquaint the attendees with the process and the challenges of language documentation, showing how the needs of the language communities and the documentary linguists map to specific NLP tasks. We will then present the state-of-the-art in NLP applied in this particularly challenging setting (extremely low-resource datasets, noisy transcriptions, limited annotations, non-standard orthographies). In doing so, we will also analyze the challenges of working in this domain and expand on both the capabilities and the limitations of current NLP approaches. Our ultimate goal is to motivate more NLP practitioners to work towards this very important direction, and also provide them with the tools and understanding of the limitations/challenges, both of which are needed in order to have an impact.
BERTatDE at SemEval-2020 Task 6: Extracting Term-definition Pairs in Free Text Using Pre-trained Model

Definition extraction is an important task in Nature Language Processing, and it is used to identify the terms and definitions related to terms.The task contains sentence classification task (i.e., classify whether it contains definition) and sequence labeling task (i.e., find the boundary of terms and definitions).The paper describes our system BERTatDE in sentence classification task (subtask 1) and sequence labeling task (subtask 2) in the definition extraction (SemEval-2020 Task 6).We use BERT to solve the multi-domain problems including the uncertainty of term boundary that is, different areas have different ways to definite the domain related terms.We use BERT, BiLSTM and attention in subtask 1 and our best result achieved 79.71% in F1 and the eighteenth place in subtask 1.For the subtask 2, we use BERT, BiLSTM and CRF to sequence labeling, and achieve 40.73% in Macro-averaged F1.
Image Compression Using Swarm Intelligence

As a result of the development in multimedia technology and direct dealing with it in social media, it has led to interest in the techniques of compacting color images because of their importance at present. Since image compression enables the representation of color image data with the fewest number of bits, which reduces transmission time in the network and increases transmission speed. To ensure the compression process is performed without loss of data, the lossless compression methods are used because no data is lost during the compression process. In this research, a new system was presented to compress the color images with efficiency and high quality. Where the swarm intelligent methods were used, as well as hybridizing it with fuzzy using the Gustafson kessel fuzzy method to improve the clustering process and create new clustering methods with fuzzy swarm intelligence to obtain the best results. Swarm algorithms were used to perform the process of clustering the image data to be compressed and then obtaining a clustered data for this image data. In contrast, a lossless compression method was used to perform the encoding of this clustered data where the huffman method was used for encoding. Four methods were applied in this research to different color and lighting images. The PSO swarm intelligent was used, which in turn was hybridized with the Gustafson kessel fuzzy method to produce a new method for fuzzy particle swarm (FPSO), as well as the grey wolf optimization method GWO, which was hybridized with Gustafson kessel and obtained a new method, which is the fuzzy grey wolf optimizer FGWO, and the results were graded efficiently from the first to the fourth method, where the FGWO method with the huffman was the most efficient depending on the standards measurement that were calculated for all methods, the compression ratio was high in this new method, in addition to the standards of MSE, RMSE, PSNR, etc. among the important measurements of the compressing process.