Reviewerly/exHarmony huggingface dataset:
Word Embedding Binarization with Semantic Information Preservation

With growing applications of Machine Learning in daily lives, Natural Language Processing (NLP) has emerged as a heavily researched area.Finding its applications in tasks ranging from simple Q/A chatbots to fully fledged conversational AI, NLP models are vital.Word and Sentence embeddings are one of the most common starting points of any NLP task.A word embedding represents a given word in a predefined vector-space while maintaining vector relations with similar or dis-similar entities.As such, different pretrained embedding such as Word2Vec, GloVe, FastText have been developed.These embeddings generated on millions of words are however very large in terms of size.Having embeddings with floating point precision also makes the downstream evaluation slow.In this paper we present a novel method to convert continuous embedding to its binary representation, thus reducing the overall size of the embedding while keeping the semantic and relational knowledge intact.This will facilitate an option of porting such big embedding onto devices where space is limited.We also present different approaches suitable for different downstream tasks based on the requirement of contextual and semantic information.Experiments have shown comparable result in downstream tasks with 7 to 15 times reduction in file size and about 5% change in evaluation parameters.
Designing Templates for Eliciting Commonsense Knowledge from Pretrained Sequence-to-Sequence Models

While internalized "implicit knowledge" in pretrained transformers has led to fruitful progress in many natural language understanding tasks, how to most effectively elicit such knowledge remains an open question.Based on the text-to-text transfer transformer (T5) model, this work explores a template-based approach to extract implicit knowledge for commonsense reasoning on multiple-choice (MC) question answering tasks.Experiments on three representative MC datasets show the surprisingly good performance of our simple template, coupled with a logit normalization technique for disambiguation.Furthermore, we verify that our proposed template can be easily extended to other MC tasks with contexts such as supporting facts in open-book question answering settings.Starting from the MC task, this work initiates further research to find generic natural language templates that can effectively leverage stored knowledge in pretrained models.
Person-in-Context Synthesis with Compositional Structural Space

Despite significant progress, controlled generation of complex images with interacting people remains difficult. Existing layout generation methods fall short of synthesizing realistic person instances; while pose-guided generation approaches focus on a single person and assume simple or known backgrounds. To tackle these limitations, we propose a new problem, Persons in Context Synthesis, which aims to synthesize diverse person instance(s) in consistent contexts, with user control over both. The context is specified by the bounding box object layout which lacks shape information, while pose of the person(s) by keypoints which are sparsely annotated. To handle the stark difference in input structures, we proposed two separate neural branches to attentively composite the respective (context/person) inputs into shared "compositional structural space", which encodes shape, location and appearance information for both context and person structures in a disentangled manner. This structural space is then decoded to the image space using multi-level feature modulation strategy, and learned in a self supervised manner from image collections and their corresponding inputs. Extensive experiments on two large-scale datasets (COCO-Stuff [6] and Visual Genome [19]) demonstrate that our framework outperforms state-of-theart methods w.r.t. synthesis quality.
Is it Great or Terrible? Preserving Sentiment in Neural Machine Translation of Arabic Reviews

Since the advent of Neural Machine Translation (NMT) approaches there has been a tremendous improvement in the quality of automatic translation. However, NMT output still lacks accuracy in some low-resource languages and sometimes makes major errors that need extensive postediting. This is particularly noticeable with texts that do not follow common lexico-grammatical standards, such as user generated content (UGC). In this paper we investigate the challenges involved in translating book reviews from Arabic into English, with particular focus on the errors that lead to incorrect translation of sentiment polarity. Our study points to the special characteristics of Arabic UGC, examines the sentiment transfer errors made by Google Translate of Arabic UGC to English, analyzes why the problem occurs, and proposes an error typology specific of the translation of Arabic UGC. Our analysis shows that the output of online translation tools of Arabic UGC can either fail to transfer the sentiment at all by producing a neutral target text, or completely flips the sentiment polarity of the target word or phrase and hence delivers a wrong affect message. We address this problem by fine-tuning an NMT model with respect to sentiment polarity showing that this approach can significantly help with correcting sentiment errors detected in the online translation of Arabic UGC.
An Intelligent Obstacle and Edge Recognition System using Bug Algorithm

Obstacle avoidance is an important task in robotics as the autonomous robot's aim is to reach the destination without collision.  One type of autonomous robot that can detect obstacles and edges and take alternative paths free of obstacles and edges is a real-time obstacle avoiding edge detection robot. This paper proposes a robotic Robot with an intelligence built into it that guides itself whenever an obstacle comes along its way by bug algorithm. This robotic Robot is constructed using AT mega 8 families’ micro-controller (Arduino Uno R3). The ultrasonic sensor is used to detect any obstacle with edges and sends a command to the microcontroller. The micro-controller, based on the received input signal, redirects the robot to push in an alternative direction by actuating the motors that are interfaced with it via a motor driver. Depending on the situation the robot is able to choose the correct path [1]. A decision making process of obstacle avoiding edge detection occurs spontaneously here. This robot was designed to think about its day-to-day potentialities.
Well-Being Data Gathering during COVID-19: Exploring the Feasibility of a Contact Tracing and Community Well-Being Safeguarding Framework

Given the need for real time data to aid in decision-making at the community level, contact tracing applications (apps) are explored as a potential method of gauging overall community well-being. The context of contact tracing effectiveness and integration with artificial intelligence is provided, as well as ideas and suggestions for how to expand for use as a community-wide data gathering approach. This commentary seeks to explore dimensions around the use of such apps to help manage in times of crisis given the widespread and destructive impacts the pandemic has on community well-being, including negative economic impacts and social declines. By connecting with community well-being, the idea of a contact tracing framework would enable communities to track data and make decisions to help foster well-being across public health, economic and social domains.
Machine Translation Reference-less Evaluation using YiSi-2 with Bilingual Mappings of Massive Multilingual Language Model

We present a study on using YiSi-2 with massive multilingual pretrained language models for machine translation (MT) reference-less evaluation. Aiming at finding better semantic representation for semantic MT evaluation, we first test YiSi-2 with contextual embed- dings extracted from different layers of two different pretrained models, multilingual BERT and XLM-RoBERTa. We also experiment with learning bilingual mappings that trans- form the vector subspace of the source language to be closer to that of the target language in the pretrained model to obtain more accurate cross-lingual semantic similarity representations. Our results show that YiSi-2’s correlation with human direct assessment on translation quality is greatly improved by replacing multilingual BERT with XLM-RoBERTa and projecting the source embeddings into the tar- get embedding space using a cross-lingual lin- ear projection (CLP) matrix learnt from a small development set.
PENGARUH SELF REGULATED LEARNING DAN WEB COURSE BERBANTUAN GOOGLE CLASSROOM, WHATSAPP GROUP TERHADAP PEMAHAMAN KONSEP

Penelitian ini bertujuan untuk mengetahui pengaruh self regulated learning terhadap pemahaman konsep, pengaruh e-learning tipe Web Course berbantuan google classroom dan whatsapp group terhadap pemahaman konsep, dan pengaruh self regulated learning dan e-learning tipe Web Course berbantuan google classroom dan whatsapp group terhadap pemahaman konsep. Penelitian ini tergolong dalam penelitian Quasi ekperimen tipe One group posttest only design. Subjek penelitian ini adalah mahasiwa semester IV sejumlah 17 mahasiswa program studi pendidikan matematika Universitas Peradaban. Pengumpulan data penelitian ini adalah observasi, tes, dan angket. Sedangkan analisis data yang digunakan adalah uji regresi dengan aplikasi SPSS. Hasil penelitian ini adalah terdapat pengaruh positif antara penerapan e-learning berbantuan google classroom dan whatsapp group terhadap kemampuan pemahaman konsep, terdapat pengaruh positif antara self regulated learning terhadap kemampuan pemahaman konsep, dan terdapat pengaruh positif antara self regulated learning dan penerapan e-learning berbantuan google classroom dan whatsapp group terhadap kemampuan pemahaman konsep. Abstract This study aims to determine the effect of self-regulated learning on concept understanding, the effect of e-learning type of Web Course assisted by google classroom and Whatsapp group on concept understanding, and the effect of self regulated learning and e-learning type of Web Course assisted by google classroom and Whatsapp group on understanding concept. This research belongs to the Quasi type One group posttest only design experiment. The subject of this research is the fourth semester students of the mathematics education study program at the University of Civilization. The research data collection was observation, test and questionnaire. While the data analysis used is a regression test with the SPSS application. The results of this study are that there is a positive influence between the implementation of e-learning assisted by google classroom and Whatsapp group on the ability to understand concepts, there is a positive influence between self regulated learning on the ability to understand concepts, and there is a positive influence between self regulated learning and the application of e-learning assisted by google classroom and Whatsapp group for the ability to understand concepts.
Alternative interpretation of color phase-shifting profilometry and a self-contained method for fringe analysis

This work aims to report a robust single-shot color fringe projection method based on phase-shifting profilometry, where, to calculate the phase, is it not necessary to have knowledge of color coupling or color imbalance calibration parameters of the projector-camera system; additionally, it does not require knowledge of the phase-shifting step, nor projecting equally stepped fringe patterns. Furthermore, it does not require a high density of carrier fringes; it only requires a composite single color image. Theoretical and experimental results are provided. Its suitability to simplify absolute unwrapping is also discussed.
On the surprising similarities between supervised and self-supervised models

How do humans learn to acquire a powerful, flexible and robust representation of objects? While much of this process remains unknown, it is clear that humans do not require millions of object labels. Excitingly, recent algorithmic advancements in self-supervised learning now enable convolutional neural networks (CNNs) to learn useful visual object representations without supervised labels, too. In the light of this recent breakthrough, we here compare self-supervised networks to supervised models and human behaviour. We tested models on 15 generalisation datasets for which large-scale human behavioural data is available (130K highly controlled psychophysical trials). Surprisingly, current self-supervised CNNs share four key characteristics of their supervised counterparts: (1.) relatively poor noise robustness (with the notable exception of SimCLR), (2.) non-human category-level error patterns, (3.) non-human image-level error patterns (yet high similarity to supervised model errors) and (4.) a bias towards texture. Taken together, these results suggest that the strategies learned through today's supervised and self-supervised training objectives end up being surprisingly similar, but distant from human-like behaviour. That being said, we are clearly just at the beginning of what could be called a self-supervised revolution of machine vision, and we are hopeful that future self-supervised models behave differently from supervised ones, and---perhaps---more similar to robust human object recognition.
Identifying Nuanced Dialect for Arabic Tweets with Deep Learning and Reverse Translation Corpus Extension System

In this paper, we present our work for the NADI Shared Task (Abdul-Mageed and Habash, 2020): Nuanced Arabic Dialect Identification for Subtask-1: country-level dialect identification. We introduce a Reverse Translation Corpus Extension Systems (RTCES) to handle data imbalance along with reported results on several experimented approaches of word and document representations and different models architectures. The top scoring model was based on AraBERT (Antoun et al., 2020), with our modified extended corpus based on reverse translation of the given Arabic tweets. The selected system achieved a macro average F1 score of 20.34% on the test set, which places us as the 7th out of 18 teams in the final ranking Leaderboard.
Hopper: Multi-hop Transformer for Spatiotemporal Reasoning

This paper considers the problem of spatiotemporal object-centric reasoning in videos. Central to our approach is the notion of object permanence, i.e., the ability to reason about the location of objects as they move through the video while being occluded, contained or carried by other objects. Existing deep learning based approaches often suffer from spatiotemporal biases when applied to video reasoning problems. We propose Hopper, which uses a Multi-hop Transformer for reasoning object permanence in videos. Given a video and a localization query, Hopper reasons over image and object tracks to automatically hop over critical frames in an iterative fashion to predict the final position of the object of interest. We demonstrate the effectiveness of using a contrastive loss to reduce spatiotemporal biases. We evaluate over CATER dataset and find that Hopper achieves 73.2% Top-1 accuracy using just 1 FPS by hopping through just a few critical frames. We also demonstrate Hopper can perform long-term reasoning by building a CATER-h dataset that requires multi-step reasoning to localize objects of interest correctly.
Specifics of Cryptocurrencies from an Accounting, Tax and Financial View in a Globalized Environment

Research background: With the advancing entrance of digital technologies into all areas of private and business life in the past 10 years a new digital asset referred to as virtual currency or cryptocurrency had been invented. This virtual currency is not yet regulated in most countries and there is a need to establish a legal framework for accounting, taxation and recording of financial transactions so the treatment of transactions with these digital assets is the same across the globalized environment, as different approaches may affect the decision-making of the management and investors or can alter the tax base for income tax purposes. Purpose of the article: The aim of this paper is to compare and discuss the different possible approaches of recording and reporting of the virtual currencies for accounting and tax purposes and to compare the approaches among the Czech Republic, the Slovak Republic and Germany to point out the need for a harmonized solution in a global environment, as dealing with cryptocurrencies is not included in the accounting and tax legal framework in many other countries. Methods: Methods of description, analysis, comparison and synthesis are used to achieve the set aim of the paper. Different approaches to the topic are demonstrated and compared in illustrative Tables. Findings &amp; Value added: The paper highlights the need for a uniform approach for the accounting and tax treatment of virtual currency by comparing the level of legal definitions and different approaches. The most elaborate legal implementation of this topic is in the Slovak Republic where the treatment of virtual currencies is included in the Accounting Act and the Income Tax Act. The Czech Republic approaches cryptocurrencies only on the basis of recommendations from the Ministry of Finance, which is not legally binding to obey. Germany has included cryptocurrencies in the Banking Act, the accounting definition is missing and the tax solution is in the Income Tax Act.
AUGMENTED REALITY-BASED LEARNING MEDIA DEVELOPMENT

The purpose of this study is to create an augmented reality based learning media. Augmented reality development on media platform is a learning media innovation that is not taking place much in Southeast Sulawesi. Augmented reality has the ability to combine virtual and real objects together so that it can create quality learning and learning. The products developed in this study are markers and augmented reality applications. The marker developed will be placed on the book. While augmented reality applications will be installed on smartphones so it ACTS as a marker reader. This research is a development study using the model Lee Owens. The object of this study is a learning media expert, a material expert, and eight students of Information Technology Education. Data collection is done using likert scale interview techniques and instruments. The results of the test assessments of the learning media experts obtain highly valid 87.20% worth of worthiness. The results of the material expert test assessments get a valid rating of 82.65%. The results of small group test assessments get an average of valid 80,89%. The assessment of the study media expert, the materials expert, and the small group test indicates that a augmented reality based media is so valid and valid that it can be used in the learning process after doing a small revision.
Research on Data-driven Clustering Analysis Fault Identification Method

摘要: 设备故障预测与健康管理已经进入了智能化时代,从设备状态监测数据中分析、提取故障特征信息,实现故障自动识别与分类是实现故障自愈调控的前提。以离心压缩机为研究对象,基于K-means聚类分析和数据驱动方法,构建K-means聚类故障模式识别模型,筛选故障识别灵敏度高的故障特征参数作为模型输入参数,基于监测数据训练得到不同故障的聚类中心,探索数据驱动的基于聚类中心距离判据的离心压缩机故障识别方法。应用中石油某公司离心压缩机正常和喘振、碰摩、不对中故障案例数据进行模型验证,结果表明:监测数据驱动的聚类分析故障识别方法能够实现离心压缩机多故障模式自动识别;与支持向量机故障识别方法相比,K-means聚类分析故障识别模型准确性更高;数据驱动方法计算实时监测数据聚类中心,利用直观距离判据实现故障模式自动识别,能够实现故障诊断和智能决策实现,为开展自愈调控提供技术支撑。
News Aggregation with Diverse Viewpoint Identification Using Neural Embeddings and Semantic Understanding Models

Today’s news volume makes it impractical for readers to get a diverse and comprehensive view of published articles written from opposing viewpoints. We introduce a transformer-based news aggregation system, composed of topic modeling, semantic clustering, claim extraction, and textual entailment that identifies viewpoints presented in articles within a semantic cluster and classifies them into positive, neutral and negative entailments. Our novel embedded topic model using BERT-based embeddings outperforms baseline topic modeling algorithms by an 11% relative improvement. We compare recent semantic similarity models in the context of news aggregation, evaluate transformer-based models for claim extraction on news data, and demonstrate the use of textual entailment models for diverse viewpoint identification.
UPB at FinCausal-2020, Tasks 1 & 2: Causality Analysis in Financial Documents using Pretrained Language Models

Financial causality detection is centered on identifying connections between different assets from financial news in order to improve trading strategies. FinCausal 2020 - Causality Identification in Financial Documents – is a competition targeting to boost results in financial causality by obtaining an explanation of how different individual events or chain of events interact and generate subsequent events in a financial environment. The competition is divided into two tasks: (a) a binary classification task for determining whether sentences are causal or not, and (b) a sequence labeling task aimed at identifying elements related to cause and effect. Various Transformer-based language models were fine-tuned for the first task and we obtained the second place in the competition with an F1-score of 97.55% using an ensemble of five such language models. Subsequently, a BERT model was fine-tuned for the second task and a Conditional Random Field model was used on top of the generated language features; the system managed to identify the cause and effect relationships with an F1-score of 73.10%. We open-sourced the code and made it available at: https://github.com/avramandrei/FinCausal2020.
Use of ICT to Enhance the Learning Process in Higher Education

ICTs in Education refers to the development of information and communications technology specifically for teaching/learning purposes, while the ICTs in education involves the adoption of general components of information and communication technologies in the teaching learning process. The National Mission on Education through Information and Communication Technology (NME-ICT), launched in 2009 by the Central Government. Let’s see how Information and Communication Technology (ICT) evolved the Higher Education system: The role of ICT in higher education, what students learn, The role of ICT in Higher Education, how Students Learn, The role of ICT in Higher Education, when students learn, The role of ICT in higher education, where students learn. Online courses, development of e-content, e-learning, digital libraries, online encyclopaedias, journals, and books would promote learning and make knowledge available to all irrespective of the distance or location or financial resources. Government intervention is necessary so that ICT can be made successful in higher education. Information and Communications Technology (ICT) has the proven power to change the world. This acronym refers to the merging of audiovisual and telephone networks with the computer single unified system of cabling.
VOCATIONAL EDUCATION AS GAINING EXPERIENCE

The article explores the importance of gaining experience in vocational education. The problem of transformation of the knowledge component in the traditional and competence-based approaches is outlined. The level model of the student’s movement in the subject is described, which unites the above-mentioned approaches, in which the logic of the formation and development of experience will act as an onto-reflexive mechanism of self-organization and transformation of the knowledge component. Whereas intelligence is a form of organizing mental experience.
A new method for solving the elliptic curve discrete logarithm problem

The elliptic curve discrete logarithm problem is considered a secure cryptographic primitive. The purpose of this paper is to propose a paradigm shift in attacking the elliptic curve discrete logarithm problem. In this paper, we will argue that initial minors are a viable way to solve this problem. This paper will present necessary algorithms for this attack. We have written a code to verify the conjecture of initial minors using Schur complements. We were able to solve the problem for groups of order up to $2^{50}$.