Reviewerly/exHarmony huggingface dataset:
Efficient Inference for Multilingual Neural Machine Translation

Multilingual NMT has become an attractive solution for MT deployment in production.But to match bilingual quality, it comes at the cost of larger and slower models.In this work, we consider several ways to make multilingual NMT faster at inference without degrading its quality.We experiment with several "light decoder" architectures in two 20language multi-parallel settings: small-scale on TED Talks and large-scale on ParaCrawl.Our experiments demonstrate that combining a shallow decoder with vocabulary filtering leads to more than ×2 faster inference with no loss in translation quality.We validate our findings with BLEU and chrF (on 380 language pairs), robustness evaluation and human evaluation.
Mapping probability word problems to executable representations

While solving math word problems automatically has received considerable attention in the NLP community, few works have addressed probability word problems specifically.In this paper, we employ and analyse various neural models for answering such word problems.In a two-step approach, the problem text is first mapped to a formal representation in a declarative language using a sequence-to-sequence model, and then the resulting representation is executed using a probabilistic programming system to provide the answer.Our best performing model incorporates general-domain contextualised word representations that were finetuned using transfer learning on another in-domain dataset.We also apply end-to-end models to this task, which bring out the importance of the two-step approach in obtaining correct solutions to probability problems.
Improving Embedding-based Large-scale Retrieval via Label Enhancement

Current embedding-based large-scale retrieval models are trained with 0-1 hard label that indicates whether a query is relevant to a document, ignoring rich information of the relevance degree.This paper proposes to improve embedding-based retrieval from the perspective of better characterizing the querydocument relevance degree by introducing label enhancement (LE) for the first time.To generate label distribution in the retrieval scenario, we design a novel and effective supervised LE method that incorporates prior knowledge from dynamic term weighting methods into contextual embeddings.Our method significantly outperforms four competitive existing retrieval models and its counterparts equipped with two alternative LE techniques by training models with the generated label distribution as auxiliary supervision information.The superiority can be easily observed on English and Chinese large-scale retrieval tasks under both standard and cold-start settings.
Anaphora Resolution in Dialogue: Description of the DFKI-TalkingRobots System for the CODI-CRAC 2021 Shared-Task

We describe the system developed by the DFKI-TalkingRobots Team for the CODI-CRAC 2021 Shared-Task on anaphora resolution in dialogue.Our system consists of three subsystems: (1) the Workspace Coreference System (WCS) incrementally clusters mentions using semantic similarity based on embeddings combined with lexical feature heuristics; (2) the Mention-to-Mention (M2M) coreference resolution system pairs same entity mentions; (3) the Discourse Deixis Resolution (DDR) system employs a Siamese Network to detect discourse anaphor-antecedent pairs.WCS achieved F1-score of 55.6% averaged across the evaluation test sets, M2M 57.2% and DDR 21.5%, all on predicted mentions.
An Intelligent Groundwater Management Recommender System

Objectives: To explore the area of groundwater that can assist to improve the accessibility of freshwater.Methods : We propose a machine-deep learning model based on a recommender system to manage and classify groundwater.Finding: The main goal of our proposed approach is to classify groundwater into multi-labels, which are drinking water (Excellent or Good) or Irrigation water (Poor or Very Poor) with guarantee a higher accuracy score.The recommender system is applied on the testing dataset and the accuracy of the deep learning technique was 91% and the accuracy of machine leaning technique was 84%.
Breast Cancer Detection Using Machine Learning Algorithms

This paper presents a computer-aided design (CAD) system that detects breast cancers (BCs). BC detection uses random forest, AdaBoost, logistic regression, decision trees, naïve Bayes and conventional neural networks (CNNs) classifiers, these machine learning (ML) based algorithms are trained to predicting BCs (malignant or benign) on BC Wisconsin data-set from the UCI repository, in which attribute clump thickness is used as evaluation class. The effectiveness of these ML algorithms are evaluated in terms of accuracy and F-measure; random forest outperformed the other classifiers and achieved 99% accuracy and 99% F-measure.
Does COVID-19 Induced Occupational Stress Moderates the Relationship of Spiritual Motivation and Academicians’ Tacit Knowledge Sharing Behaviour Among South Asian Higher Education Institutions?

The COVID outbreak has frazzled many employers to research how to upkeep and boost the performance outcomes continuously. It is even trickier in Higher Education Institutions (HEIs) when they have hibernated from conventional to cyber universities due to the COVID pandemic. It is relatable to motivate and retain the knowledge workers persistently. Therefore, this study is designed to investigate the impact of academicians' spiritual motivation on their tacit knowledge sharing behaviour, with the moderation of COVID-induced occupational stress. The population embraced the academicians of Pakistani Higher Education Institutions. ~289 academicians were considered as the sample size. Descriptive and inferential statistics (linear regression) were performed in SPSS, whereas; 'Hayes' process macro' was used to test moderation. The findings designate that academicians' spiritual motivation is significantly and positively associated with tacit knowledge sharing behaviour—moreover, COVID-induced occupational stress moderates their relationship. Consequently, the results bequeath the policymakers and the relevant authorities of HEIs, with valuable propositions on how to upkeep academicians' tacit knowledge sharing behaviour by considering COVID-induced occupational stress as eu-stress if they are spiritually motivated. Profuse suggestions were also presented to future researchers to further look into the anticipated study model.
Reconstruction Attack on Instance Encoding for Language Understanding

A private learning scheme TextHide was recently proposed to protect the private text data during the training phase via so-called instance encoding.We propose a novel reconstruction attack to break TextHide by recovering the private training data, and thus unveil the privacy risks of instance encoding.We have experimentally validated the effectiveness of the reconstruction attack with two commonly-used datasets for sentence classification.Our attack would advance the development of privacy preserving machine learning in the context of natural language processing.
Analysis of domestic and foreign trends of individualization of the educational process in the university: analytical report

Introduction. The individualization of the education process is today one of the priority directions of the transformation of the higher education system, which involves the creation of conditions for the implementation of the educational opportunities of students, their academic mobility, the design of an individual educational trajectory, i.e. on the one hand, it serves as a tool for fulfilling the state order in the formation of a competitive specialist, and on the other hand, it allows you to maximally orient education to the personality of the student. Materials and Methods. The article considers domestic and foreign research materials aimed at organizing and implementing the individualization of the learning process. The work uses methods of analysis of scientific literature, questionnaires, analysis, generalization and systematization of data. Results . The authors identified and described the main trends in the development of individualization of the educational process, the relationship between the concepts of "individual educational route" and "individual educational trajectory". A comparative analysis of the results of a questionnaire survey of teachers and students on the problem of individualization of education is carried out, and the main models of individualization of the educational process are highlighted. Discussion and Conclusions. As challenges for the individualization of the educational process of higher pedagogical education, the authors see the bridging of the "digital divide" arising from the inequality in access to digital tools between the subjects of the educational space of the university, as well as regions and countries; updating the goals and content of education, as well as forms, methods and technologies of organizing the educational process; changing approaches and tools to assessing the competencies and educational results of students in the course of mastering an individual educational route; changing the roles of the subjects of the educational process.
Explaining Machine Learning by Bootstrapping Partial Dependence Functions and Shapley Values

Machine learning and artificial intelligence methods are often referred to as "black boxes" when compared to traditional regression-based approaches.However both traditional and machine learning methods are concerned with modeling the joint distribution between endogenous (target) and exogenous (input) variables.The fitted models are themselves functionals of the data, about which we can do statistical inference using computationally intensive methods such as the bootstrap.Where linear models describe the fitted relationship between the target and input variables via the slope of that relationship (coefficient estimates), the same fitted relationship can be described rigorously for any machine learning model by first-differencing the partial dependence functions.Bootstrapping these first-differenced functionals provides standard errors and confidence intervals for the estimated relationships.We show that this approach replicates the point estimates of coefficients attained in a linear OLS models, and demonstrate how this generalizes to marginal relationships in ML/AI models.This paper extends the partial dependence plot
TEMP: Taxonomy Expansion with Dynamic Margin Loss through Taxonomy-Paths

As an essential form of knowledge representation, taxonomies are widely used in various downstream natural language processing tasks.However, with the continuously rising of new concepts, many existing taxonomies are unable to maintain coverage by manual expansion.In this paper, we propose TEMP, a self-supervised taxonomy expansion method, which predicts the position of new concepts by ranking the generated taxonomy-paths.For the first time, TEMP employs pre-trained contextual encoders in taxonomy construction and hypernym detection problems.Experiments prove that pre-trained contextual embeddings are able to capture hypernym-hyponym relations.To learn more detailed differences between taxonomy-paths, we train the model with dynamic margin loss by a novel dynamic margin function.Extensive evaluations exhibit that TEMP outperforms prior state-of-the-art taxonomy expansion approaches by 14.3% in accuracy and 15.8% in mean reciprocal rank on three public benchmarks.
Towards an e-learning ecologies approach to pedagogy in a post-COVID world

In this article, part of the special COVID-19 issue of the Journal of Education, we concentrate on digital technology as one of the core dimensions of education's pandemic-related response. As the default teaching mode during the pandemic, Online Emergency Remote Teaching evoked contentious responses about future education directions in a post-COVID world. We shed light on the role of digital technology in South African education, specifically in relation to current debates on higher education. We present an argument that supports an approach based on e-learning ecologies to pedagogy to inform teaching and learning in institutional contexts. We argue that a reflexive pedagogy-led response to digital technology holds promise for creating a systemic educational approach to promoting students' critical epistemic engagement to enable them to secure viable futures.
Non-institutional humanities, philosophical practice, informal education: the contours of the educational creative industry

Introduction. Non-institutional humanities, philosophical practice, and informal education are “a family like” phenomena that are outside the social institution of science and education and are adjacent to socio-cultural activities and social work. The purpose of the article is to outline the contours of the informal educational creative industry in the postmodern society, which combines non-institutional humanities, philosophical practice, and informal education. Methods. The author uses the methods of autobiographical reflection, comparative analysis, empirical observation and analysis of the primary sources of non-institutional humanities. Scientific novelty of the research. The transition to nonmaterial work of the postmodern era is the reason for the relevance of the area under study. The growing demand of a contemporary for such issues as the meaning of life, happiness, the growth of the precariat, the ousting of philosophy from formal education constitute a new social reality that determines the novelty of the research in the informal educational creative industry. Results. The author classifies non-institutional humanities according to the spheres of its existence and the strategies of subject genesis presented in the teachings. The concept of dedifferentiation is considered as an explanation of the area under study in postmodernism. The specificity of the philosophical approach in the work of a practicing philosopher is considered. Conclusions. The author connects the prospects of the informal educational creative industry with institutionalization in the way of leisure, entertainment, and other personal services. The author makes conclusions about the differences between formal and informal education, and indicates the risks and opportunities for the development of the informal philosophical sector.
Evaluasi Tingkat Penerimaan Sistem Manajemen Aset Menggunakan Metode HOT-FIT

The development of the institution will be directly proportional to the development of the number and types of assets it owns. The growing number and type of assets owned by an institution will have an impact on increasingly difficult management. In asset management, Diponegoro University has developed an Sistem Informasi Manajemen Aset Terpadu (SIMASET). SIMASET needs to be evaluated to find out its shortcomings and can be input for further development. This study aims to determine the factors that influence the level of acceptance and net benefits received by users from the application of SIMASET using the Human-Organization-Technology (HOT-Fit) method. This study begins with designing hypotheses, determining the sample of respondents, filling out questionnaires, and ending with questionnaire data analysis. There are 20 hypotheses tested regarding the relationship between technology, human and organizational constructs in the HOT-Fit method. The Partial Least Squares-Structural Equation Modeling (PLS-SEM) method in the Smart-PLS 3.0 application is used to analyze the questionnaire data that has been filled out by SIMASET direct users. The results showed that the net benefits of implementing SIMASET were increasing effectiveness, helping decision making, reducing errors and facilitating communication. In addition, it can be seen that SIMASET's shortcomings lie in the quality of information and service quality because they have no significant effect on system use and user satisfaction. SIMASET acceptance rate of 51.6% or moderate taken from the R-square value of net benefits.
InVeRo-XL: Making Cross-Lingual Semantic Role Labeling Accessible with Intelligible Verbs and Roles

Notwithstanding the growing interest in crosslingual techniques for Natural Language Processing, there has been a surprisingly small number of efforts aimed at the development of easy-to-use tools for cross-lingual Semantic Role Labeling.In this paper, we fill this gap and present InVeRo-XL, an off-the-shelf state-of-the-art system capable of annotating text with predicate sense and semantic role labels from 7 predicate-argument structure inventories in more than 40 languages.We hope that our system -with its easy-to-use RESTful API and Web interface -will become a valuable tool for the research community, encouraging the integration of sentencelevel semantics into cross-lingual downstream tasks.InVeRo-XL is available online at http: //nlp.uniroma1.it/invero.
The Topic Confusion Task: A Novel Evaluation Scenario for Authorship Attribution

Authorship attribution is the problem of identifying the most plausible author of an anonymous text from a set of candidate authors.Researchers have investigated same-topic and cross-topic scenarios of authorship attribution, which differ according to whether new, unseen topics are used in the testing phase.However, neither scenario allows us to explain whether errors are caused by a failure to capture authorship writing style or by a topic shift.Motivated by this, we propose the topic confusion task where we switch the author-topic configuration between the training and testing sets.This setup allows us to distinguish two types of errors: those caused by the topic shift and those caused by the features' inability to capture the writing styles.We show that stylometric features with part-of-speech tags are the least susceptible to topic variations.We further show that combining them with other features leads to significantly lower topic confusion and higher attribution accuracy.Finally, we show that pretrained language models such as BERT and RoBERTa perform poorly on this task and are surpassed by simple features such as word-level n-grams.
Object Detection With Component-Graphs in Multi-Band Images: Application to Source Detection in Astronomical Images

In the context of mathematical morphology, component-graphs are complex but powerful structures for multi-band image modeling, processing, and analysis.In this work, we propose a novel multi-band object detection method relying on the component-graphs and statistical hypothesis tests.Our analysis shows that component-graphs are better at capturing image structures compared to the classical component-trees, with significantly higher detection capacity.Besides, we introduce two filtering algorithms to identify duplicated and partial nodes in the component-graphs.The proposed method, applied to the detection of sources on astronomical images, demonstrates a significant improvement in detecting faint objects on both multi-band simulated and real astronomical images compared to the state of the art.
Neural Re-rankers for Evidence Retrieval in the FEVEROUS Task

Computational fact-checking has gained a lot of traction in the machine learning and natural language processing communities.A plethora of solutions have been developed, but methods which leverage both structured and unstructured information to detect misinformation are of particular relevance.In this paper, we tackle the FEVEROUS (Fact Extraction and VERification Over Unstructured and Structured information) challenge which consists of an open source baseline system together with a benchmark dataset containing 87,026 verified claims.We extend this baseline model by improving the evidence retrieval module yielding the best evidence F1 score among the competitors in the challenge leaderboard while obtaining an overall FEVEROUS score of 0.20 (5 th best ranked system).
DEVELOPING WRITING SKILLS THROUGH ONLINE SELF-REGULATED LEARNING

As a side effect of the emergency transition to remote online learning, all Indonesian schools and universities have stopped teaching in classroom settings in mid-March 2020 and began using internet platforms to deliver online learning activities. However, there are issues concerning students' perseverance and achievement as fully online learning is implemented. Considering many learners may not be able to concentrate on studying under traumatic circumstances, such as a pandemic, it is necessary to consider learning strategies that produce learning opportunities for all students to achieve specific goals in distance learning. Writing skills are considered the most challenging of all language skills among English as foreign language (EFL) students. When students use an online self-regulated learning (OSEL) strategy in learning writing, they can control their performance and benefit from online learning by adjusting their behaviors. Thus, the aim of the study is to find the causal relationship between OSEL strategy and writing skills.
Model perilaku pencarian informasi (analisis teori perilaku pencarian informasi menurut David Ellis)

Introduction. This paper will discuss about information seeking behavior. Information is the target that is sought after by all people. The amount of information becomes a necessity for humans. This information need refers to the public or users who really need information. Information needs can be used for all circles without exception. From this information need, information seeking behavior will arise as a means of seeking information to obtain the information you want to find. Behavior Information behavior is a pattern of human behavior in terms of the nature of information, both in the involvement of using and in the search for information. The behavior referred to here is how a person gets information through his behavior.Data Collection Methods. The method used in this paper is literature study where the data sources of this paper are books, magazines, journals and references related to information seeking behavior according to David Ellis so that it can be analyzed carefully. The process of collecting data goes through 3 processes, namely editing, organizing and findingData Analysis In this paper, the author analyzes the data using the opinion or theory of David Ellis in information seeking behavior as the main referenceResults and Discussion. Information seeking behavior according to David Ellis has several characteristics including starting as the initial stage in information search, chaining which is the stage to browse literature through quotes from books or journals, browsing which is the tracing stage, differentiating which is the screening stage of the sources obtained.,monitoring as a means of monitoring developments, extracting as a means of continuing the search, verifying which is the stage of checking the information that has been obtained, and finally ending as a cover for searching and searching for information.Conclusion. Based on the findings, David Ellis shows that there are 8 characteristics of information seeking behavior, namely starting chaining browsing, differentiating monitoring, extracting, verifying ending.