Reviewerly/exHarmony huggingface dataset:
Optimizing LMS CANVAS for Interactive Online Learning Perceived by the Students

Recently, technology has been an integral part of education. Teachers must use technology into their teaching practices in order to adapt to society's digital revolution. One of the strategies that can be implemented by the teachers is utilizing Canvas as one of Learning Management Systems (henceforth LMS) in their course. This study aims to investigate the students’ perception towards the use of Canvas to promote interactive online learning. Implementing case study as the research design, this study involved 77 students as the participants, and used both questionnaire and semi-structured interview to collect the data. In this study, the quantitative data were analyzed using descriptive statistics, while the qualitative data were analyzed using thematic analysis. The findings of this study revealed that students accessed Canvas ranging for less than 1 hour to more than 2 hours in a week. They accessed it mostly to join some learning activities designed by the teacher, including reading the learning modules, joining online discussion, and doing the assignments given by the teachers. They also believe that during the online learning, Canvas is an easy-to-use LMS, and it provides a lot of features that help them learning. Besides, they also agree that Canvas is effective to provide them the opportunity have an interactive learning experience. In addition, they also perceived that using Canvas motivated them, and make the learning process more interesting. Therefore, teachers can optimize various features provided by Canvas to make the teaching and learning process in online setting become more interactive, interesting, and effective.
Coupling Context Modeling with Zero Pronoun Recovering for Document-Level Natural Language Generation

Natural language generation (NLG) tasks on pro-drop languages are known to suffer from zero pronoun (ZP) problems, and the problems remain challenging due to the scarcity of ZP-annotated NLG corpora.In this case, we propose a highly adaptive two-stage approach to couple context modeling with ZP recovering to mitigate the ZP problem in NLG tasks.Notably, we frame the recovery process in a task-supervised fashion where the ZP representation recovering capability is learned during the NLG task learning process, thus our method does not require NLG corpora annotated with ZPs.For system enhancement, we learn an adversarial bot to adjust our model outputs to alleviate the error propagation caused by mis-recovered ZPs.Experiments on three document-level NLG tasks, i.e., machine translation, question answering, and summarization, show that our approach can improve the performance to a great extent, and the improvement on pronoun translation is very impressive.
Fine-Tuning Word Embeddings for Hierarchical Representation of Data Using a Corpus and a Knowledge Base for Various Machine Learning Applications

Word embedding models have recently shown some capability to encode hierarchical information that exists in textual data. However, such models do not explicitly encode the hierarchical structure that exists among words. In this work, we propose a method to learn hierarchical word embeddings (HWEs) in a specific order to encode the hierarchical information of a knowledge base (KB) in a vector space. To learn the word embeddings, our proposed method considers not only the hypernym relations that exist between words in a KB but also contextual information in a text corpus. The experimental results on various applications, such as supervised and unsupervised hypernymy detection, graded lexical entailment prediction, hierarchical path prediction, and word reconstruction tasks, show the ability of the proposed method to encode the hierarchy. Moreover, the proposed method outperforms previously proposed methods for learning nonspecialised, hypernym-specific, and hierarchical word embeddings on multiple benchmarks.
Assessing the Eligibility of Backtranslated Samples Based on Semantic Similarity for the Paraphrase Identification Task

In the domain of natural language augmentation, the eligibility of generated samples remains not well understood.To gather insights around this eligibility issue, we apply a transformer-based similarity calculation within the BET framework based on backtranslation, in the context of automated paraphrase detection.While providing a rigorous statistical foundation to BET, we push their results by analyzing statistically the impacts of the level of qualification, and several sample sizes.We conducted a vast amount of experiments on the MRPC corpus using six pre-trained models: BERT, XLNet, Albert, RoBERTa, Electra, and DeBerta.We show that our method improves significantly these "base" models while using only a fraction of the corpus.Our results suggest that using some of those smaller pretrained models, namely RoBERTa base and Electra base, helps us reach F1 scores very close to their large counterparts, as reported on the GLUE benchmark.On top of acting as a regularizer, the proposed method is efficient in dealing with data scarcity with improvements of around 3% in F1 score for most pre-trained models, and more than 7.5% in the case of Electra.
Single Image Deraining With Continuous Rain Density Estimation

Single image deraining (SIDR) often suffers from over/under deraining due to the nonuniformity of rain densities and the variety of raindrop scales. In this paper, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">co</i> ntinuous <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">de</i> nsity-guided network (CODE-Net) for SIDR. Particularly, it is composed of a rain streak extractor and a denoiser, where the convolutional sparse coding (CSC) is exploited to filter out noises from the extracted rain streaks. Inspired by the reweighted iterative soft-threshold (ISTA) for CSC, we address the problem of continuous rain density estimation by learning the weights with channel attention blocks from sparse codes. We further develop a multiscale strategy to depict rain streaks appearing at different scales. Experiments on synthetic and real-world data demonstrate the superiority of our methods over recent state-of-the-arts, in terms of both quantitative and qualitative results. Additionally, instead of quantizing rain density with several levels, our CODE-Net can provide continuous-valued estimations of rain densities, which is more desirable in real applications.
One Size Does Not Fit All: Finding the Optimal Subword Sizes for FastText Models across Languages

Unsupervised representation learning of words from large multilingual corpora is useful for downstream tasks such as word sense disambiguation, semantic text similarity, and information retrieval.The representation precision of log-bilinear fastText models is mostly due to their use of subword information.In previous work, the optimization of fast-Text's subword sizes has not been fully explored, and non-English fastText models were trained using subword sizes optimized for English and German word analogy tasks.In our work, we find the optimal subword sizes on the English, German, Czech, Italian, Spanish, French, Hindi, Turkish, and Russian word analogy tasks.We then propose a simple gram coverage model and we show that it predicts better-than-default subword sizes on the Spanish, French, Hindi, Turkish, and Russian word analogy tasks.We show that the optimization of fastText's subword sizes matters and results in a 14% improvement on the Czech word analogy task.We also show that expensive parameter optimization can be replaced by a simple -gram coverage model that consistently improves the accuracy of fastText models on the word analogy tasks by up to 3% compared to the default subword sizes, and that it is within 1% accuracy of the optimal subword sizes.
Enhancing Performance Evaluation for Video Plagiarism Detection Using Local Feature through SVM and KNN algorithm

Nowadays in the digital world, there are lots of videos being uploaded to video, and social media sharing platforms are growing exponentially.About the Internet and multimedia technologies, illicitly copied content is a serious social problem.Since the internet is accessible to everyone, it is easy to download content and re-upload it.Copying videos from the internet can be considered plagiarism.In this paper, a method is proposed for feature extraction of video plagiarism detection.This framework is based on the local features to identify the videos frame by frame with the videos stored in the database.It becomes important to review the existing video plagiarism detection methods, compare them through appropriate performance metrics, list out their pros and cons and state the open challenges.First of all, it will pre-process the data with the help of SIFT and OCR Feature extraction.After that, the system applies the video retrieval and detection function using the two classifier algorithm the SVM, and the KNN.In the first stage, when the query is compared to all training data, KNN calculates the distances between the query and its neighbors and selects the K nearest neighbors.It is applied in the second stage to recognize the object using the SVM algorithm.Here we use the VSD dataset to predict the plagiarized videos.And the accuracy of these plagiarized videos after comparing them is 98%.
Semiparametric maximum likelihood probability density estimation

A comprehensive methodology for semiparametric probability density estimation is introduced and explored. The probability density is modelled by sequences of mostly regular or steep exponential families generated by flexible sets of basis functions, possibly including boundary terms. Parameters are estimated by global maximum likelihood without any roughness penalty. A statistically orthogonal formulation of the inference problem and a numerically stable and fast convex optimization algorithm for its solution are presented. Automatic model selection over the type and number of basis functions is performed with the Bayesian information criterion. The methodology can naturally be applied to densities supported on bounded, infinite or semi-infinite domains without boundary bias. Relationships to the truncated moment problem and the moment-constrained maximum entropy principle are discussed and a new theorem on the existence of solutions is contributed. The new technique compares very favourably to kernel density estimation, the diffusion estimator, finite mixture models and local likelihood density estimation across a diverse range of simulation and observation data sets. The semiparametric estimator combines a very small mean integrated squared error with a high degree of smoothness which allows for a robust and reliable detection of the modality of the probability density in terms of the number of modes and bumps.
Emotion Inference in Multi-Turn Conversations with Addressee-Aware Module and Ensemble Strategy

Emotion inference in multi-turn conversations aims to predict the participant's emotion in the next upcoming turn without knowing the participant's response yet, and is a necessary step for applications such as dialogue planning.However, it is a severe challenge to perceive and reason about the future feelings of participants, due to the lack of utterance information from the future.Moreover, it is crucial for emotion inference to capture the characteristics of emotional propagation in conversations, such as persistence and contagiousness.In this study, we focus on investigating the task of emotion inference in multi-turn conversations by modeling the propagation of emotional states among participants in the conversation history, and propose an addresseeaware module to automatically learn whether the participant keeps the historical emotional state or is affected by others in the next upcoming turn.In addition, we propose an ensemble strategy to further enhance the model performance.Empirical studies on three different benchmark conversation datasets demonstrate the effectiveness of the proposed model over several strong baselines.
Assessing the Generalization Capacity of Pre-trained Language Models through Japanese Adversarial Natural Language Inference

Despite the success of multilingual pre-trained language models, it remains unclear to what extent these models have human-like generalization capacity across languages.The aim of this study is to investigate the out-ofdistribution generalization of pre-trained language models through Natural Language Inference (NLI) in Japanese, the typological properties of which are different from those of English.We introduce a synthetically generated Japanese NLI dataset, called the Japanese Adversarial NLI (JaNLI) dataset, which is inspired by the English HANS dataset and is designed to require understanding of Japanese linguistic phenomena and illuminate the vulnerabilities of models.Through a series of experiments to evaluate the generalization performance of both Japanese and multilingual BERT models, we demonstrate that there is much room to improve current models trained on Japanese NLI tasks.Furthermore, a comparison of human performance and model performance on the different types of gardenpath sentences in the JaNLI dataset shows that structural phenomena that ease interpretation of garden-path sentences for human readers do not help models in the same way, highlighting a difference between human readers and the models.
Locality defeats the curse of dimensionality in convolutional teacher–student scenarios*

Abstract Convolutional neural networks perform a local and translationally-invariant treatment of the data: quantifying which of these two aspects is central to their success remains a challenge. We study this problem within a teacher–student framework for kernel regression, using ‘convolutional’ kernels inspired by the neural tangent kernel of simple convolutional architectures of given filter size. Using heuristic methods from physics, we find in the ridgeless case that locality is key in determining the learning curve exponent β (that relates the test error ϵ t ∼ P − β to the size of the training set P ), whereas translational invariance is not. In particular, if the filter size of the teacher t is smaller than that of the student s , β is a function of s only and does not depend on the input dimension. We confirm our predictions on β empirically. We conclude by proving, under a natural universality assumption, that performing kernel regression with a ridge that decreases with the size of the training set leads to similar learning curve exponents to those we obtain in the ridgeless case.
Google-trickers, Yaminjeongeum, and Leetspeak: An Empirical Taxonomy for Intentionally Noisy User-Generated Text

WARNING: This article contains contents that may offend the readers.Strategies that insert intentional noise into text when posting it are commonly observed in the online space, and sometimes they aim to let only certain community users understand the genuine semantics.In this paper, we explore the purpose of such actions by categorizing them into tricks, memes, fillers, and codes, and organize the linguistic strategies that are used for each purpose.Through this, we identify that such strategies can be conducted by authors for multiple purposes, regarding the presence of stakeholders such as 'Peers' and 'Others'.We finally analyze how these strategies appear differently in each circumstance, along with the unified taxonomy accompanying examples.
Mitigating False-Negative Contexts in Multi-document Question Answering with Retrieval Marginalization

Question Answering (QA) tasks requiring information from multiple documents often rely on a retrieval model to identify relevant information for reasoning.The retrieval model is typically trained to maximize the likelihood of the labeled supporting evidence.However, when retrieving from large text corpora such as Wikipedia, the correct answer can often be obtained from multiple evidence candidates.Moreover, not all such candidates are labeled as positive during annotation, rendering the training signal weak and noisy.This problem is exacerbated when the questions are unanswerable or when the answers are Boolean, since the model cannot rely on lexical overlap to make a connection between the answer and supporting evidence.We develop a new parameterization of set-valued retrieval that handles unanswerable queries, and we show that marginalizing over this set during training allows a model to mitigate false negatives in supporting evidence annotations.We test our method on two multi-document QA datasets, IIRC and HotpotQA.On IIRC, we show that joint modeling with marginalization improves model performance by 5.5 F1 points and achieves a new state-of-the-art performance of 50.5 F1.We also show that retrieval marginalization results in 4.1 QA F1 improvement over a non-marginalized baseline on HotpotQA in the fullwiki setting.
Improving Multilingual Neural Machine Translation with Auxiliary Source Languages

Multilingual neural machine translation models typically handle one source language at a time.However, prior work has shown that translating from multiple source languages improves translation quality.Different from existing approaches on multi-source translation that are limited to the test scenario where parallel source sentences from multiple languages are available at inference time, we propose to improve multilingual translation in a more common scenario by exploiting synthetic source sentences from auxiliary languages.We train our model on synthetic multi-source corpora and apply random masking to enable flexible inference with single-source or bi-source inputs.Extensive experiments on Chinese/English→Japanese and a large-scale multilingual translation benchmark show that our model outperforms the multilingual baseline significantly by up to +4.0 BLEU with the largest improvements on low-resource or distant language pairs.
Universal-KD: Attention-based Output-Grounded Intermediate Layer Knowledge Distillation

Intermediate layer matching is shown as an effective approach for improving knowledge distillation (KD).However, this technique applies matching in the hidden spaces of two different networks (i.e.student and teacher), which lacks clear interpretability.Moreover, intermediate layer KD cannot easily deal with other problems such as layer mapping search and architecture mismatch (i.e. it requires the teacher and student to be of the same model type).To tackle the aforementioned problems all together, we propose Universal-KD to match intermediate layers of the teacher and the student in the output space (by adding pseudo classifiers on intermediate layers) via the attention-based layer projection.By doing this, our unified approach has three merits: (i) it can be flexibly combined with current intermediate layer distillation techniques to improve their results (ii) the pseudo classifiers of the teacher can be deployed instead of extra expensive teacher assistant networks to address the capacity gap problem in KD which is a common issue when the gap between the size of the teacher and student networks becomes too large; (iii) it can be used in cross-architecture intermediate layer KD.We did comprehensive experiments in distilling BERT-base into BERT-4, RoBERTa-large into DistilRoBERTa and BERT-base into CNN and LSTM-based models.Results on the GLUE tasks show that our approach is able to outperform other KD techniques.
Interconnection of linguistics and musical art

The terminology of music semiology, which is an academic discipline with a significant educational resonance and a necessary component of music and educational practice at its higher educational and qualification levels, lies at the intersection of the main aspects of musicology such as the history of music, the theory and analysis of musical forms, music aesthetics and the theory of music interpretation, and others. Music semiology covers the transitional methodological abilities due to its subject reference points and the wide range of the material involved in the cognitive field. Music semiology can be considered a necessary basic discipline for the professional training of musicologists and practicing musicians of any programme. There is no doubt about the importance of mastering the language system, which is fundamental in the chosen field of communication. As an academic discipline, music semiology can be presented according to the way its terminology is built, it includes three main themes among which each of the following continues the meaning of the previous one by deepening and enlarging, detailing in an analytical way. Scientific novelty is determined by the fact that unlike music semiotics and the theory of music semantics.
Talent Management Employee Development by Using Certainty Factor Method of Expert System

Talent management is a factor that determines success in the business environment because talent management requires quantitative and qualitative skills. This study aims to implement the certainty factor (CF) method of an expert system for employee development talent management. This research using a certainty factor (CF) method to design an expert system framework. Due to the focus on our research aim, we provide a certainty factor calculation with mathematical modeling for calculating talent management employee development in X Company. The confidence level is 93.55% for a recommendation of not promotion of the job; for 52.38% is a recommendation that can be proposed for promotion, but HRD will evaluate in some time; for 98.73% is a recommendation for promotion of the job. We used CF calculation that can provide the level of confidence (in %). The calculation of the certainty factor (CF) method can be used for recommending job promotion in some companies.
Capturing Speaker Incorrectness: Speaker-Focused Post-Correction for Abstractive Dialogue Summarization

In this paper, we focus on improving the quality of the summary generated by neural abstractive dialogue summarization systems.Even though pre-trained language models generate well-constructed and promising results, it is still challenging to summarize the conversation of multiple participants since the summary should include a description of the overall situation and the actions of each speaker.This paper proposes self-supervised strategies for speaker-focused post-correction in abstractive dialogue summarization.Specifically, our model first discriminates which type of speaker correction is required in a draft summary and then generates a revised summary according to the required type.Experimental results show that our proposed method adequately corrects the draft summaries, and the revised summaries are significantly improved in both quantitative and qualitative evaluations.