Reviewerly/exHarmony huggingface dataset:
An Intelligent Interactive Support System for Word Usage Learning in Second Languages

Second-language learners typically encounter difficulty in learning how to use words properly. One reason for this is that words that express multiple meanings - that is, polysemous words - vary from language to language. For example, the word "figure" can mean either a number, an image, or a person. However, this is not the case in all languages. Although the word "figure" is often used with both meanings, some words have only rare usage cases. Thus, ideally, a second language vocabulary learner would benefit from the following learning aids. For words all meanings of which are frequently used, all of the senses of the word should be presented with a high learning priority, whereas senses of a word that are rarely used should be given a low priority. Furthermore, learners should be able to visually understand the semantic proximity of word senses. In this study, we propose an intelligent, interactive user interface to support learning such word usages in second languages. The proposed interface estimates the difficulty of the usage example of the word by estimating its exceptionality. Our method measures semantic closeness with contextualized word embeddings. The model also incorporates a deep anomaly detection model to measure the exceptionality of each usage example. Using our interface, learners of English as a second language (ESL) learners can learn about the semantic closeness between word usage examples and the exceptionality of the examples.
Deep Learning or Deep Ignorance? Comparing Untrained Recurrent Models in Educational Contexts

The development and application of deep learning methodologies has grown within educational contexts in recent years. Perhaps attributable, in part, to the large amount of data that is made available through the adoption of computer-based learning systems in classrooms and larger-scale MOOC platforms, many educational researchers are leveraging a wide range of emerging deep learning approaches to study learning and student behavior in various capacities. Variations of recurrent neural networks, for example, have been used to not only predict learning outcomes but also to study sequential and temporal trends in student data; it is commonly believed that they are able to learn high-dimensional representations of learning and behavioral constructs over time, such as the evolution of a students' knowledge state while working through assigned content. Recent works, however, have started to dispute this belief, instead finding that it may be the model's complexity that leads to improved performance in many prediction tasks and that these methods may not inherently learn these temporal representations through model training. In this work, we explore these claims further in the context of detectors of student affect as well as expanding on existing work that explored benchmarks in knowledge tracing. Specifically, we observe how well trained models perform compared to deep learning networks where training is applied only to the output layer. While the highest results of prior works utilizing trained recurrent models are found to be superior, the application of our untrained-versions perform comparably well, outperforming even previous non-deep learning approaches.
What Is Relevant for Learning? Approximating Readers’ Intuition Using Neural Content Selection

Experienced readers intuitively mark text passages containing central concepts as learning-relevant when reading actively. Although this intuitive process of marking important information is sometimes imperfect, it fosters comprehension. It would be beneficial to approximate this intuition by automatically detecting potential learning-relevant content. It is a building block for various upstream tasks such as automatic self-assessment or intelligent author assistance. This work argues that learners often apply heuristics based on different sentence types to determine the learning-relevant contents in texts. We show that such heuristics can be approximated using neural sentence classifiers and implement two neural sentence classifiers detecting causal and definitory sentences. We evaluate the classifiers' ability to detect learning-relevant information in an empirical study (N = 37). Furthermore, a system performance evaluation compares the proposed classifiers with unsupervised summarization systems. We find evidence for a small but reliable association between the chosen automatically detectable sentence types (definition/causal) and the learners' perception of content relevance. Additionally, the classifiers outperform most other relevant content selection techniques in our experiments. Interestingly, other simple heuristics based on sentence position or length also exhibit strong performance.
Raising Student Completion Rates with Adaptive Curriculum and Contextual Bandits

We present an adaptive learning Intelligent Tutoring System, which uses model-based reinforcement learning in the form of contextual bandits to assign learning activities to students. The model is trained on the trajectories of thousands of students in order to maximize their exercise completion rates and continues to learn online, automatically adjusting itself to new activities. A randomized controlled trial with students shows that our model leads to superior completion rates and significantly improved student engagement when compared to other approaches. Our approach is fully-automated unlocking new opportunities for learning experience personalization.
Towards Aligning Slides and Video Snippets: Mitigating Sequence and Content Mismatches

Slides are important form of teaching materials used in various courses at academic institutions. Due to their compactness, slides on their own may not stand as complete reference materials. To aid students' understanding, it would be useful to supplement slides with other materials such as online videos. Given a deck of slides and a related video, we seek to align each slide in the deck to a relevant video snippet, if any. While this problem could be formulated as aligning two time series (each involving a sequence of text contents), we anticipate challenges in generating matches arising from differences in content coverage and sequence of content between slide deck-video pairs. To mitigate these challenges, we propose a two-stage algorithm that builds on time series alignment to filter out irrelevant content and to align out-of-sequence slide deck and video pairs. We experiment with real-world datasets from openly available lectures, which have been manually annotated with start and end times of each slide in the videos to facilitate the evaluation of matches.
A regularization‐based deep unsupervised model for affine multimodal <scp>co‐registration</scp>

Abstract Extensive network receptive field is key for unsupervised affine registration because instead of deformable registration that takes care of local subtleties, the affine registration is global so that the last layers need to see big patches of the organ‐in‐interest. To extend the network's receptive field, we need to go for deeper networks, which causes producing complex models. On the other hand, affine transformation is restricted by its low degree‐of‐freedom (DoF) where larger models increasingly develop the hazard of overfitting. To worsen the situation, the regularizer module cannot be applied to the affine transformation with such a restricted DoF. In this paper, we propose a differentiable computational layer to convert the affine transformation outputted by the network to its corresponding dense displacement field. Such an affine‐to‐field layer enables us to apply different regularization terms on the outputted transformation in order to avoid the overfitting phenomenon while deepening the network. The proposed approach was evaluated on an annotated hard multimodal dataset containing 1109 pairs of CT/MR images of the brain with different heterogeneity for example, variety in scanners, setups and resolutions. Based on the results, the proposed customized layer is fully successful to handle the overfitting for deeper networks that are able to produce richer transformations than the shallower networks from different evaluation metrics for example, in target registration error the proposed network with seven layers has a 13.3% (or 9.1 mm) improvement in performance. The implementation of the proposed customized affine‐to‐field layer in the Python, Keras package with the Tensorflow backend can be publically accessed via https://github.com/boveiri/Deep-coReg .
Humanist Literacy in Critical Reading Activities as an Alternative Direction for Future Language Learning

Humanist literacy is an awareness of reading and understanding human phenomena that can be created through critical reading activities. Critical reading activities aim to reflect on the reading results so that they can make wise decisions on how to think and behave humanely. Decision-making skills need to be improved through critical reading learning with a humanist literacy approach. The problem so far is the unavailability of future learning designs by integrating humanist literacy through critical reading activities. Therefore, this study focuses on how to integrate humanist literacy in critical reading activities as an alternative direction for future language learning. The purpose of this study was to describe the grand design of integrating humanist literacy in critical reading activities as an alternative direction for future language learning. This research is a development research of one part of the development research procedure. The data for this study were sourced from the comments of lecturers and students from questionnaires distributed online. This research instrument was developed based on the concept of language learning, characteristics of humanist literacy, and characteristics of critical reading that have been validated. Data analysis was carried out critically reflectively by using tagCrow and AntCont applications. The results of the study prove that the grand design of integrating humanist literacy in critical reading activities can be carried out through communicating humanist literacy, collaborating humanist literacy, and humanist critical thinking with sub-skills of analyzing, interpreting, inferring, evaluating, explaining, and regulating oneself in a humanistic manner to make decisions. wisely according to the principles of humanistic life.
Contextualized Recommendation of Aviation Ancillary Services Based on Passenger Portraits

Ancillary services are crucial to Chinese domestic airlines’ profits. Personalized recommendations for ancillary aviation services must consider massive amounts of contextualized online and offline data. The issue of sparsity is more serious in massive data, which makes traditional collaborative filtering (CF) algorithms less accurate. This article adds instant contextualized travel-related factors to the modeling of air passengers’ ancillary services, thus achieving dynamic recommendations. It also proposes a four-tuple to construct a contextual ontology as a conceptual model of ancillary aviation services. The article establishes a context-aware air passenger portrait model based on four tag sets, including passenger attribute tags, ancillary service attribute tags, passenger interaction behavior tags, and contextual attribute tags. Further, it proposes a multidimensional method for calculating contextual portrait similarity; the method recommends a list of the top- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> items to the target passenger according to the similarity calculation. Compared with the traditional CF recommendation method, the proposed algorithm has better performance, improving the accuracy rate by 4.3% and the recall rate by 2.62%. The final experimental result demonstrates that the contextual recommendation method achieves higher passenger satisfaction, which can provide consolidated data and decision support for the management of aviation ancillary services.
Exploring the Limits of Hard Example Mining for ID Document to Selfie Matching

Most face verification systems verify a person’s identity by comparing the ID document with the live face (also called spot face). More specifically, the spot face can be regarded as the probe image and the face in ID document can be regard as the reference image. The identity verification is then conducted by calculating the similarity of the two images. This problem is called ID vs. Spot (IvS) face recognition. IvS face recognition is different from general face recognition, where the IvS face datasets usually contain a very large number of identities (up to millions and more) with only two images per identity. We adopt a metric learning way rather than a classification-based way to train the network on IvS datasets, aiming to avoid tremendous pressure on GPU resource. In this work, we improve the performance mainly from two aspects, namely extending the selecting space to select very hard samples and increasing the diversity of selected samples. For the former, we propose a Super Batch (S-Batch) by aggregating many traditional batches together, in which each anchor sample can select the hard sample pairs from a very large batch. Moreover, we employ a Cross-Batch Hard Example Mining (CBHEM) to select hard samples from not only current batch but also historical batches. For the latter, we propose a Various Batch Sizes (VBS) in our S-Batch, which selects hard samples of different batch scales for training. Extensive experiments demonstrate the effectiveness of our method on IvS face recognition.
Knowledge is Power: An Improvement of Recommendation Combining Collaborative Filtering with Knowledge Graph

The rapid development of the Internet has caused the problem of information overload. In response to this problem, personalized recommendation has emerged, and the collaborative filtering algorithm has become the most widely used recommendation algorithm by virtue of its simplicity and efficiency. However, collaborative filtering algorithm suffers the problems of cold start and data sparsity, while knowledge graph as a new type of side information can be a good solution to this problem. Based on this, this paper proposed a PTransE-CF model, which captured semantic information contained in the knowledge graph through the path-based PTransE algorithm, and used the item-based collaborative filtering algorithm to obtain the similarity of users’ rating information on items. By fusing the similarity of semantics and the similarity of users’ ratings, the fused similarity was obtained for personalized recommendation, and the experiment on the real dataset MovieLens-1M confirmed that the PTransE-CF model improved the accuracy and interpretability of the collaborative filtering recommendation model and confirmed the effectiveness of the model.
Literature Review of Cryptocurrencies

Bitcoin was first introduced by Nakamoto,(2008), as a peer-to- peer electronic system to do online payments from one party to the other without the intervention of a financial institution. The market for digital crypto currencies such as Bitcoin, Litecoin, Ripple, Monero, Ethereum and Shiba has grown since 2008 in terms of the number of new currencies as well as the market capitalization and transaction frequency measured by the Poisson and Binominal distribution. All digital currencies are used as a medium of exchange and compete with fiat money as the US dollar without interacting with a financial institution. They are forming a decentralized peer- to-peer payment system. The fiat money is issued by a central bank. In contrast, digital currencies are autonomous and as used as a medium of exchange they affect the monetary policy of fiat money. Digital currencies can be used to pay goods and services or as an investment in a portfolio with other assets such as stocks, bonds, gold, oil or commodities. In this paper, we focus on the literature review of crypto currencies to help the reader and the investor to better understand how the market is working and the recent research that has been conducted.
A Novel Local Adaptive Percentage Split Distribution Method for Image Binarization and Classification

Binary thresholding methods separate image pixels into two groups as 0s or 1s. The two types of binary thresholding methods are global thresholding and local thresholding. Global thresholding methods are appropriate for binarizing the images that has smooth and contrast distribution of pixels. The performance of global thresholding struggles with distorted and tampered images as it introduces additional noise and causes variation in contrast and illumination. Local adaptive thresholding methods address the issue with every pixel a threshold based on the contrast distribution of neighboring pixels. This paper introduces Local Adaptive Percentage Split Distribution (LAPSD) method for binarization. LAPSD computes threshold based on percentage wise split of neighboring pixels. The performance of LAPSD is compared with benchmark binary thresholding methods such Bradley’s, Niblack’s, and Sauvola’s against PSNR, SSIM and MSE metrics. The accuracy of LAPSD image binarization is measured using Convolution Neural Network (CNN) models and the results prove that the performance of the proposed method surpasses traditional methods in all means.
Low-Light Image Enhancement : A Survey on the Concepts and Methods Used

With the rapid growth in real time applications needing a robust image enhancement system, so has the need for low light image enhancement increased. The need for low light image enhancement spans across all fields including real time applications like autonomous driving, surveillance footage and crime scene investigations showing that the need for processing of such footage is inevitable. As computer vision algorithms are widely applicable and used, the demand for low light image and video enhancement is more so than ever. This paper reviews and provides an overview of such different low light image enhancement techniques, elaborating them based on their methodology, characteristics, applicability and their scope for improvement as well.
An Emotion-Based Design Approach to Tackle Cold Start Challenge of Movie Recommendation System

Feelings-related data is an effective indicator of users' preferences. An improved movie recommender-based on users' feelings was efficiently used to overcome the cold start challenge. The ensemble learning algorithm (ELA) with different input models (i.e., random forest (RF), gradient boosting (GB), stochastic gradient descent (SGD), K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), and support vector machine (SVM)) was applied to transform the intensity of users' emotions (i.e., happiness, sadness, anger, disgust, fear, and surprise to equivalent genres (i.e., action, comedy, documentary, horror, drama, thriller and western) through the voting classifier. The proposed model capability was assessed based on four statistical criteria of accuracy, precision, recall, mean accuracy-coverage harmony (F1-score), and area-under-the-curve (AUC). The confusion matrix showed that the best prediction algorithms respectively were RF, SGD, DT and SVM, whereas a low efficiency was found for KNN, LR, and GB models after passing the voting classifier. Excluding three feeble models led to a significant increase in transforming emotions to genres with improved values of accuracy (), precision (), recall (), and F1 score (). Also, the AUC of this revolutionized ELM model was significantly increased to 95.3% (p < 0.05). The newly developed model could well predict the users' emotions transformation to horror (99.0%), action (96.8%), western (96.4%), thriller (94.0%), drama (92.7%), comedy (91.5%), and documentary (89.6%). The final selection algorithm along with input parameters successfully opted for five top movies based on the new users’ feelings.
The use of optimised SVM method in human abnormal behaviour detection

The study aims to improve the performance of the recognition algorithm for human behaviours. An improved Support Vector Machine (SVM) behaviour recognition method based on dynamic and static characteristics is studied, and video surveillance is used to track and test human targets. In video frames, the average background method is used to model the static background, and the optical flow is used to model the dynamic background. In terms of target tracking, a multi-feature particle filter is used. And an improved Fuzzy Support Vector Machine (FSVM) is used for behaviour recognition based on the combination of dynamic and static characteristics. The results show that the integration of dynamic and static characteristics of human behaviour can comprehensively show human behavioural characteristics. And experiments are carried out on the KTH data set, and the detection accuracy increases by 2.05%.
RAID-Net: Region-Aware Image Deblurring Network Under Guidance of the Image Blur Formulation

Image deblurring is a challenging field in computational photography and computer vision. In the deep learning era, deblurring methods boosted with neural networks achieve significant results. However, the existing methods mainly focus on solving specific image deblurring problem, and overlook the origin of the motion blur. In this paper, we revisit how blur occurs, and divide them into three categories, i.e. caused by relative motion between camera and scene, caused by the movement of the object itself and the edges of a blurring image, which may meet discontinuity because of the pixels trajectory sampled outside the image. To address the issues of different blurs in an image, we propose a two-stage neural network for image deblurring named RAID-Net. In order to remove the global blurry region caused by camera movements, we first use a U-shape network to get the coarse deblurred image. Then we leverage an adaptive reasoning module to model the relationship between different blurry regions within one image jointly and remove the other two categories of motion blur. Experiments on two public benchmark datasets demonstrate that our method achieves comparable or better results over the state-of-the-art methods.
High-Quality White Laser Diode Fabricated by Laser-Driven Tricolor PiG Film-on-Sapphire

Phosphor-converted white laser diode (WLD) is considered as a promising light source in next-generation high-brightness lighting. However, the WLD suffers from low optical quality in luminous efficacy and color rendering. In this work, we fabricated a high-quality WLD by a blue laser-driven a tricolor phosphor-in-glass (PiG) film-on-sapphire (PIGS). The tricolor PiGS was prepared by sintering a green/yellow/red phosphor glass film on a sapphire substrate. The optical quality of tricolor PiGS-WLDs were optimized by adjusting the phosphor ratio. At the phosphor ratio of 9:6:1, the PiGS-WLD achieves high optical quality with a luminous flux of 362.1 lm, a color rendering index (CRI) of 80.3, and a correlated color temperature (CCT) of 4590 K at a laser power of 2.4 W. The PiGS-WLD has the highest luminous efficacy of 154.5 lm/W at 3.15 W. Furthermore, the PiGS-WLD displays excellent color stability with small CCT and CRI deviation under various laser powers and continuous excitation. The surface temperature of PiGS-WLD is stable at 104 °C due to the high thermal conductivity of sapphire substrate. Therefore, this study provides a stable and high-quality laser-driven white light source for high-brightness lighting and display.