Reviewerly/exHarmony huggingface dataset:
Efektivitas Penggunaan LMS Live Unpad Sebagai Media Pembelajaran Daring

This study aims to determine the various features or teaching instruments contained in the Learning Management System (LMS) Learning in Virtual Environment (LiVE) Universitas Padjadjaran (Unpad). Since the Covid-19 pandemic in Indonesia forced all educational institutions to conduct face-to-face learning from their homes, Unpad has maximized the use of Unpad's LiVE for e-learning learning systems. Darin in Wahono, 2003 states that e-learning is a type of teaching and learning that allows the delivery of teaching materials to students using the internet, intranet or other computer network media. This research is descriptive qualitative by using literature study and experience method. The results show that various features of LiVE are very easy to use and function to communicate related to learning to students and can be used as a benchmark for student activity. And the e-learning system will be a system that will continue to be used in accordance with the times that demand digital transformation in various fields of life, especially education.
Research Guides: Open Access: Open Access Reference

This subject guide is intended to serve as a learning tool to educate our users on open access and the changing world of scholarly communications.
A multilabel approach to morphosyntactic probing.

We propose using a multilabel probing task to assess the morphosyntactic representations of multilingual word embeddings. This tweak on canonical probing makes it easy to explore morphosyntactic representations, both holistically and at the level of individual features (e.g., gender, number, case), and leads more naturally to the study of how language models handle co-occurring features (e.g., agreement phenomena). We demonstrate this task with multilingual BERT (Devlin et al., 2018), training probes for seven typologically diverse languages: Afrikaans, Croatian, Finnish, Hebrew, Korean, Spanish, and Turkish. Through this simple but robust paradigm, we verify that multilingual BERT renders many morphosyntactic features simultaneously extractable. We further evaluate the probes on six held-out languages: Arabic, Chinese, Marathi, Slovenian, Tagalog, and Yoruba. This zero-shot style of probing has the added benefit of revealing which cross-linguistic properties a language model recognizes as being shared by multiple languages.
Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus.

The development of automated approaches to linguistic acceptability has been greatly fostered by the availability of the English CoLA corpus, which has also been included in the widely used GLUE benchmark. However, this kind of research for languages other than English, as well as the analysis of cross-lingual approaches, has been hindered by the lack of resources with a comparable size in other languages. We have therefore developed the ItaCoLA corpus, containing almost 10,000 sentences with acceptability judgments, which has been created following the same approach and the same steps as the English one. In this paper we describe the corpus creation, we detail its content, and we present the first experiments on this new resource. We compare in-domain and out-of-domain classification, and perform a specific evaluation of nine linguistic phenomena. We also present the first cross-lingual experiments, aimed at assessing whether multilingual transformer-based approaches can benefit from using sentences in two languages during fine-tuning.
Research on Bioengineering Algorithm Based on Deep Learning Neural Network

Deep learning (DL) is a fresh study orientation in the field of machine learning in computer science. It is recommend into machine learning to make it nearer to the customary artificial target intelligence. DL is the inherent law and express level of learning sample data, and the information get in the learning procedure is of mighty help to the explain of data such as words, images and sounds. CNN (Convolutional Neural Network) combines feature extraction with itemize process to train neural network, which has acquire mighty successful in the field of image classification. This paper focuses on the automatic classification of fetal facial ultrasound images. A 19-layer convolution network is proposed and improved. By using data enhancement, adding global mean pooling layer, reducing the number of channels in the full connection layer of the model, and optimizing learning based on parameter transfer learning of fine-tuning training, the automatic classification of fetal facial ultrasound images with limited data volume can be realized. Match with the present solutions, the depth network proposed in this paper can effectively avoid ultrasonic noise interference and learn deep features more effectively. A heavy quantity of specific analysis test have proved its effectiveness.
LibGuides: A LibGuide for Film Studies: Tutorials

Welcome to the film studies LibGuide! If you are a film studies student or simply have an interest for film, this guide will help you out.
Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval

We present Mr. TyDi, a multi-lingual benchmark dataset for mono-lingual retrieval in eleven typologically diverse languages, designed to evaluate ranking with learned dense representations. The goal of this resource is to spur research in dense retrieval techniques in non-English languages, motivated by recent observations that existing techniques for representation learning perform poorly when applied to out-of-distribution data. As a starting point, we provide zero-shot baselines for this new dataset based on a multi-lingual adaptation of DPR that we call “mDPR”. Experiments show that although the effectiveness of mDPR is much lower than BM25, dense representations nevertheless appear to provide valuable relevance signals, improving BM25 results in sparse–dense hybrids. In addition to analyses of our results, we also discuss future challenges and present a research agenda in multi-lingual dense retrieval. Mr. TyDi can be downloaded at https://github.com/castorini/mr.tydi.
On Generalization in Coreference Resolution

While coreference resolution is defined independently of dataset domain, most models for performing coreference resolution do not transfer well to unseen domains. We consolidate a set of 8 coreference resolution datasets targeting different domains to evaluate the off-the-shelf performance of models. We then mix three datasets for training; even though their domain, annotation guidelines, and metadata differ, we propose a method for jointly training a single model on this heterogeneous data mixture by using data augmentation to account for annotation differences and sampling to balance the data quantities. We find that in a zero-shot setting, models trained on a single dataset transfer poorly while joint training yields improved overall performance, leading to better generalization in coreference resolution models. This work contributes a new benchmark for robust coreference resolution and multiple new state-of-the-art results.
RETRACTED: Lightweight Visualization of 3D Model Based on Cloud Computing Information System

Abstract The processing requirements of 3D model data in modern manufacturing industry and the data type characteristics of 3D model data source. With the popularity of modern cloud computing platform and the technical advantages of cloud computing platform, the 3D model information is stored in the information system, and the cloud computing technology is used to manage the information system. The purpose of this article is to combine cloud computing technology with lightweight 3D visualization technology such as acrobat 3D to realize lightweight visualization of 3D model in information system. With the advantage of cloud computing platform which does not occupy local resources and has large data processing capacity combined with lightweight 3D visualization technology such as Acrobat 3D,3D model data can be visualized in cloud fast traffic. This paper investigates and studies, compares the speed of reading information between the information system using cloud computing technology and the ordinary local storage information system, and tries to find out the advantages of cloud computing compared with the traditional information system. In addition, this paper also optimizes lightweight on the basis of 3D model data set. Reduce the collective product of its data without destroying the visualization of its basic information.
Overcoming Conflicting Data when Updating a Neural Semantic Parser

In this paper, we explore how to use a small amount of new data to update a task-oriented semantic parsing model when the desired output for some examples has changed. When making updates in this way, one potential problem that arises is the presence of conflicting data, or out-of-date labels in the original training set. To evaluate the impact of this understudied problem, we propose an experimental setup for simulating changes to a neural semantic parser. We show that the presence of conflicting data greatly hinders learning of an update, then explore several methods to mitigate its effect. Our multi-task and data selection methods lead to large improvements in model accuracy compared to a naive data-mixing strategy, and our best method closes 86% of the accuracy gap between this baseline and an oracle upper bound.
Open-domain clarification question generation without question examples.

An overarching goal of natural language processing is to enable machines to communicate seamlessly with humans. However, natural language can be ambiguous or unclear. In cases of uncertainty, humans engage in an interactive process known as repair: asking questions and seeking clarification until their uncertainty is resolved. We propose a framework for building a visually grounded question-asking model capable of producing polar (yes-no) clarification questions to resolve misunderstandings in dialogue. Our model uses an expected information gain objective to derive informative questions from an off-the-shelf image captioner without requiring any supervised question-answer data. We demonstrate our model’s ability to pose questions that improve communicative success in a goal-oriented 20 questions game with synthetic and human answerers.
Active Model of Ruling Education

the article describes active modern educational model of ruling technology looked through as a cybernetic point of view
Common Sense Bias in Semantic Role Labeling

Large-scale language models such as ELMo and BERT have pushed the horizon of what is possible in semantic role labeling (SRL), solving the out-of-vocabulary problem and enabling end-to-end systems, but they have also introduced significant biases. We evaluate three SRL parsers on very simple transitive sentences with verbs usually associated with animate subjects and objects, such as, “Mary babysat Tom”: a state-of-the-art parser based on BERT, an older parser based on GloVe, and an even older parser from before the days of word embeddings. When arguments are word forms predominantly used as person names, aligning with common sense expectations of animacy, the BERT-based parser is unsurprisingly superior; yet, with abstract or random nouns, the opposite picture emerges. We refer to this as “common sense bias” and present a challenge dataset for evaluating the extent to which parsers are sensitive to such a bias. Our code and challenge dataset are available here: github.com/coastalcph/comte
Word Sense Disambiguation for Large Documents Using Neural Network Model

Lexical ambiguity in natural language processing is a live problem and needs to be resolved. The lexical ambiguity mainly occurred because of polysemous words. Because of polysemous words, Machine fails to understand the user queries and generates ambiguous results. This research aims to rectify the ambiguous queries with corrected queries by using supervised machine learning approach. The input query is preprocessed first with sentence splitting, tokenization, lemmatization, and stemming. From the filtered output the ambiguous words are detected and context information is stored into local generated data structures. The adaptive word vector is constructed with respect to the ambiguous word and stored context information. While constructing adaptive word vector the mean-similarity and max-similarity of the ambiguous words is considered. The adaptive word vector is provided as an input to the supervised neural network model for the classification. The corrected sense values are available at the out layer. These sense values are mapped with freely available lexical dataset WordNet for getting the most correct meaning of ambiguous words. The ultimate goal of this research is to provide unambiguous communication in between human and machine.
What Went Wrong? Explaining Overall Dialogue Quality through Utterance-Level Impacts

Improving user experience of a dialogue system often requires intensive developer effort to read conversation logs, run statistical analyses, and intuit the relative importance of system shortcomings. This paper presents a novel approach to automated analysis of conversation logs that learns the relationship between user-system interactions and overall dialogue quality. Unlike prior work on utterance-level quality prediction, our approach learns the impact of each interaction from the overall user rating without utterance-level annotation, allowing resultant model conclusions to be derived on the basis of empirical evidence and at low cost. Our model identifies interactions that have a strong correlation with the overall dialogue quality in a chatbot setting. Experiments show that the automated analysis from our model agrees with expert judgments, making this work the first to show that such weakly-supervised learning of utterance-level quality prediction is highly achievable.
Cybercrime and its Moral Implications in the Light of Kantian Ethics

This work has its thrust on the moral and ethical implication of cyber crime on the society. The present disturbing states of cyber crimes as well as the apparent degradation of the moral values are clear indications and pointers to the obvious social problems bedeviling us today. Globally, cyber criminals are using the computer gadget and internet to defrauding people. This act remains a potent threat to the wellbeing of the individual and the society at large. The activities of cyber criminals, no doubt, have led to the growth of unstable social structure, economic dilemma and other forms of threats to internet users and the society at large. In conducting this research, the method of analysis was both critical and expository of the problem of cyber crimes and its moral and ethical implication on the society. The discovery from the conduct of this research shows that many crimes are perpetuated through the use of computer. Such crimes may include defrauding of private business, hacking into personal or company account, changing of another person's bio-data, interfering into another person's profile without permission, using the internet to deceive and lie, as well as indulge in different kinds of evils. Our findings reveal that the society today is rapidly undergoing serious moral degeneration because of the high rate at which cyber criminals are comfortable with these immoral acts despite established legal laws as punishment and moral codes as guide against cyber criminal activities. These evil acts are both morally and legally condemnable.
The Global Banking Standards QA Dataset (GBS-QA)

A domain specific question answering (QA) dataset dramatically improves the machine comprehension performance. This paper presents a new Global Banking Standards QA dataset (GBS-QA) in the banking regulation domain. The GBS-QA has three values. First, it contains actual questions from market players and answers from global rule setter, the Basel Committee on Banking Supervision (BCBS) in the middle of creating and revising banking regulations. Second, financial regulation experts analyze and verify pairs of questions and answers in the annotation process. Lastly, the GBS-QA is a totally different dataset with existing datasets in finance and is applicable to stimulate transfer learning research in the banking regulation domain.
Job recruitment system

Every year, over 200,000 students graduate from higher education institution. Shockingly, 1 out of 5 graduates remain unemployed, majority from it is a Degree holders. Nowadays, being a university graduate no longer guarantees a job. As the number of graduates continues to climb each year, students are facing tough competition amongst a shaky economy. This system is developed to help the fresh graduates to find a suitable job even before their graduation and for the employers to post the job advertisements. There are three type of users that can access into the system which are student, company and admin. This Job Recruitment System is for students to register, find jobs, apply for the job, manage their resume, and manage their profile information. While, the company or employer can register, upload job advertisements, manage resume, search the desired students and manage account user. Other than that, the admin will manage the accounts of students and the company. The objective of this system is to analyze and design the current problem of this system by produce Software Requirement Specification (SRS), to develop a prototype system in Job Recruitment System and to validate the proposed prototype system in web based. Rapid Application Development (RAD) is the methodology used to develop this system. RAD consists of four stages. First is requirement planning stage, followed by user design stage, rapid construction stage and lastly is transition stage. After the development of the system, client will be given the user acceptance test (UAT) to ensure that all functions work according to the requirement without any error. The UAT testing ensure the system has achieved the objective and be able to solve the problem that happened before.
Predictive Analysis of Breast Cancer Based on Stacking Algorithm

With the development of computers, machine learning algorithms can be applied in the medical field to solve many classification and prediction problems, thus assisting professionals to quickly judge and diagnose the disease.In this paper, we propose a breast cancer prediction model based on stacking algorithm, which integrates several traditional machine learning algorithms and compares with Adaboosting, SVM and other algorithms in terms of accuracy, ROC curve, PR curve, F1 value index, etc.The experiments show that the accuracy of the breast cancer classification model based on stacking algorithm can reach 97.23%, which is 6% higher than the classification accuracy of SVM, Adaboosting and other algorithms, and the AUC value of ROC curve can be improved by up to 0.26, which provides a certain reference value in breast cancer prediction examination and so on.
Online Learning over Time in Adaptive Neural Machine Translation

Adaptive Machine Translation purports to dynamically include user feedback to improve translation quality.In a post-editing scenario, user corrections of machine translation output are thus continuously incorporated into translation models, reducing or eliminating repetitive error editing and increasing the usefulness of automated translation.In neural machine translation, this goal may be achieved via online learning approaches, where network parameters are updated based on each new sample.This type of adaptation typically requires higher learning rates, which can affect the quality of the models over time.Alternatively, less aggressive online learning setups may preserve model stability, at the cost of reduced adaptation to user-generated corrections.In this work, we evaluate different online learning configurations over time, measuring their impact on user-generated samples, as well as separate in-domain and out-of-domain datasets.Results in two different domains indicate that mixed approaches combining online learning with periodic batch fine-tuning might be needed to balance the benefits of online learning with model stability.
Evaluation of Summarization Systems across Gender, Age, and Race

Summarization systems are ultimately evaluated by human annotators and raters. Usually, annotators and raters do not reflect the demographics of end users, but are recruited through student populations or crowdsourcing platforms with skewed demographics. For two different evaluation scenarios – evaluation against gold summaries and system output ratings – we show that summary evaluation is sensitive to protected attributes. This can severely bias system development and evaluation, leading us to build models that cater for some groups rather than others.
Research on the impact of trade uncertainty on national grain supply and risk cost control

In order to explore the impact of trade uncertainty on the national grain supply, based on the BP neural network algorithm, this paper combines the characteristics of grain supply and trade data to construct an intelligent analysis model. Moreover, this paper uses historical data as the basic data to study the trade uncertainty and the relevant characteristics of grain supply, and combine the actual demand to construct the structure of the BP neural network model. After constructing the model, this paper analyzes the function of the model, combines the comparative analysis method to classify the data and studies the influence mechanism of trade uncertainty on the national grain supply. In addition, this paper combines the actual data to analyze the performance of the algorithm model of this paper, and the analysis results are consistent with historical data. Finally, this paper analyzes the cost control of national grain supply based on the results of the model analysis, and proposes several countermeasures.