Reviewerly/exHarmony huggingface dataset:
Automatic Question Generation from Video

We live in a world of information. Lecture videos available online have become a major source of information. The evaluation of knowledge gained requires questions to be generated from the videos referred. This paper aims to generate questions from online educational videos to facilitate self-learning through a continuous evaluation process and to deliver a light-weight, faster Video Question generator model as the already existing methodologies are heavier, costlier and require high computational power. The paper integrates the state-of-the-art technology in NLP, the T5 transformers, to incorporate transfer learning and making the model more flexible. The system can develop Yes or no questions and who, what, where, when, why, which, and how (Wh) questions.
Continuous Gaze Tracking With Implicit Saliency-Aware Calibration on Mobile Devices

Gaze tracking is a useful human-to-computer interface, which plays an increasingly important role in a range of mobile applications. Gaze calibration is an indispensable component of gaze tracking, which transforms the eye coordinates to the screen coordinates. The existing approaches of gaze tracking either have limited accuracy or require the user's cooperation in calibration and in turn hurt the quality of experience. We in this paper propose vGaze, continuous gaze tracking with implicit saliency-aware calibration on mobile devices. The design of vGaze stems from our insight on the temporal and spatial dependent relation between the visual saliency and the user's gaze. vGaze is implemented as a light-weight software that identifies video frames with “useful” saliency information, sensing the user's head movement, performs opportunistic calibration using only those “useful” frames, and leverages historical information for accelerating saliency detection. We implement vGaze on a commercial mobile device and evaluate its performance in various scenarios. The results show that vGaze can work at real time with video playback applications. The average error of gaze tracking is 1.51cm (2.884 <inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> ) which decreases to 0.99cm (1.891 <inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> ) with historical information and 0.57cm (1.089 <inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> ) with an indicator.
Learning Bias-reduced Word Embeddings Using Dictionary Definitions

Pre-trained word embeddings, such as GloVe, have shown undesirable gender, racial, and religious biases.To address this problem, we propose DD-GloVe, a train-time debiasing algorithm to learn word embeddings by leveraging dictionary definitions.We introduce dictionaryguided loss functions that encourage word embeddings to be similar to their relatively neutral dictionary definition representations.Existing debiasing algorithms typically need a precompiled list of seed words to represent the bias direction, along which biased information gets removed.Producing this list involves subjective decisions and it might be difficult to obtain for some types of biases.We automate the process of finding seed words: our algorithm starts from a single pair of initial seed words and automatically finds more words whose definitions display similar attributes traits.We demonstrate the effectiveness of our approach with benchmark evaluations and empirical analyses.
Weighted Metamorphosis for Registration of Images with Different Topologies

We present an extension of the Metamorphosis algorithm to align images with different topologies and/or appearances. We propose to restrict/limit the metamorphic intensity additions using a time-varying spatial weight function. It can be used to model prior knowledge about the topological/appearance changes (e.g., tumour/oedema). We show that our method improves the disentanglement between anatomical (i.e., shape) and topological (i.e., appearance) changes, thus improving the registration interpretability and its clinical usefulness. As clinical application, we validated our method using MR brain tumour images from the BraTS 2021 dataset. We showed that our method can better align healthy brain templates to images with brain tumours than existing state-of-the-art methods. Our PyTorch code is freely available here: https://github.com/antonfrancois/Demeter_metamorphosis .
Is There Anything Interesting to Say About Dimensions in Physics?

The theory of dimensions in physics is astonishingly rich.It can be viewed at different levels of abstraction and, at any of these levels, reveals deep suggestions.The relevant theory was initially developed to get a useful mean to reduce the number of variables in experiments.Within this context Rayleigh method and Buckingham Π theorem are highly conceptual working tools.Further elements of novelty have emerged during the last years and methods, directly or indirectly, linked to dimensional analysis, have become a central issue to treat families of differential equations, to enter deeply in the so-called scaling relations characterizing the phenomenology, not only in physics but in social science, economy, biology, and so on.This article is an effort aimed at providing a reasonably comprehensive account of the theory and the relevant practical outcomes, which spans over a large variety of topics including classical issues in hydrodynamics but also in general relativity and quantum mechanics as well.
Don’t Tamper with Dual System Encryption

In related-key attacks (RKA), an attacker modifies a secret key stored in a device by tampering or fault injection and observes the evaluation output of the cryptographic algorithm based on this related key. In this work, we show that the dual system encryption methodology of Waters (Crypto 2009) fits well with RKA security. We apply simple modifications to a regularly-secure identity-based encryption (IBE) scheme (TCC 2010) constructed through dual system to achieve RKA security for rational functions, which is beyond the polynomial barrier of Bellare et al. ’s framework (Asiacrypt 2012). We achieve security by pushing the complexity of RKA directly down to the underlying intractability assumption. We also discuss how to extend it to a hierarchical IBE scheme that remains secure against RKA over identity-based secret keys beyond the master secret, albeit under some structural constraints.
Restricted or Not: A General Training Framework for Neural Machine Translation

Restricted machine translation incorporates human prior knowledge into translation.It restricts the flexibility of the translation to satisfy the demands of translation in specific scenarios.Existing work typically imposes constraints on beam search decoding.Although this can satisfy the requirements overall, it usually requires a larger beam size and far longer decoding time than unrestricted translation, which limits the concurrent processing ability of the translation model in deployment, and thus its practicality.In this paper, we propose a general training framework that allows a model to simultaneously support both unrestricted and restricted translation by adopting an additional auxiliary training process without constraining the decoding process.This maintains the benefits of restricted translation but greatly reduces the extra time overhead of constrained decoding, thus improving its practicality.The effectiveness of our proposed training framework is demonstrated by experiments on both original (WAT21 En↔Ja) and simulated (WMT14 En→De and En→Fr) restricted translation benchmarks.
KnowCommerce: A Semantic Web Compliant Knowledge-driven Paradigm for Product Recommendation in E-commerce

Product recommendation is changing the way how e-commerce Web sites function and also the way how the products are advertised in a way that maximizes the profit by showing the targeted product to the target audience by making use of the user queries and user activity in the Web site. This paper proposes a semantically driven technique for product recommendation using knowledge engineering combined with deep learning and optimization algorithms. The dataset that is used for training the recommendation system is the users click data and user query which is combined into a set called an item configuration set which is later used to create an e-commerce ontology whose semantic similarity is compared to the neural network’s output, and using this similarity score, products are recommended to the users. The efficiency of the architecture is analyzed in comparison with the baseline approaches, and it is shown that the suggested method outruns the performance, with an F-measure and FDR of 93.08% and 93.72% accordingly.
EM-LAST: Effective Multidimensional Latent Space Transport for an Unpaired Image-to-Image Translation With an Energy-Based Model

For an unpaired image-to-image translation task to work well, the latent space of each image domain must be well designed, and the codes of the style must be translated toward the target with the preservation of the parts corresponding to the source content.In general, most variational autoencoder (VAE)-based models use one-dimensional latent space.However, to apply high dimensional methodologies such as vector quantization, which is a recent trend in deep learning, it is necessary to be able to control multidimensional latent space.Among the VAE-based models using relatively complex multidimensional latent spaces, we apply the methodology of an energy-based model (EBM) with vector quantized VAE v2 (VQ-VAE-2) as the main model.Our study shows that among the latent spaces that represent each image domain, the importance of each feature at the top and bottom latent spaces corresponding to the high-and low-level features must be interpreted differently for proper translation.We also present various analyses and visual outcomes of multidimensional latent space transport.
Data-Aware Adaptive Pruning Model Compression Algorithm Based on a Group Attention Mechanism and Reinforcement Learning

The success of convolutional neural networks (CNNs) benefits from the stacking of convolutional layers, which improves the model&#x2019;s receptive field for image data but also causes a decrease in inference speed. To improve the inference speed of large convolutional network models without sacrificing performance indicators too much, a data-aware adaptive pruning algorithm is proposed. The algorithm consists of two parts, namely, a channel pruning method based on the attention mechanism and a data-aware pruning policy based on reinforcement learning. Experimental results on the CIFAR-100 dataset show that the performance of the proposed pruning algorithm is reduced by only 2.05%, 1.93% and 5.66% after pruning the VGG19, ResNet56 and EfficientNet networks, respectively, but the speedup ratios are 3.63, 3.35, and 1.14, respectively, and the comprehensive pruning performance is the best. In addition, the generalization ability of the reconstruction model is evaluated on the ImageNet dataset and FGVC Aircraft dataset, and the performance of the proposed algorithm is the best, which shows that the proposed algorithm learns data-related information in the pruning process, that is, it is a data-aware algorithm.
Digital Competence and Teacher Training Overview: Is Lithuania Ready for Digitalism in Education?

The aim of this study is to research the state-of-the-art situation of digital competence of both in-service and pre-service teachers, as well as students of the Lithuanian primary and secondary education. The analysis relies on scientific literature, i.e., peer-reviewed scientific articles, employing both quantitative and qualitative research methods as well as analysis of documents, published in foreign and Lithuanian scientific journals, mainly in the past decade. The results of the systematic literature analysis reveal that the contemporary context of technology-enhanced smart pedagogy in Lithuania still seems to be insufficient to assure integration and access to smart educational environments to all children. Although the situation currently is changing to the best at a quick pace, more research is needed along with implications for policymakers and pedagogical study programme implementers for continuous development of the digital competence.
Flower Pollination Algorithm for Feature Selection in Tweets Sentiment Analysis

Text-based social media platforms have developed into important components for communication between customers and businesses.Users can easily state their thoughts and evaluations about products or services on social media.Machine learning algorithms have been hailed as one of the most efficient approaches for sentiment analysis in recent years.However, as the number of online reviews increases, the dimensionality of text data increases significantly.Due to the dimensionality issue, the performance of machine learning methods has been degraded.However, traditional feature selection methods select attributes based on their popularity, which typically does not improve classification performance.This work presents a population-based metaheuristic for feature selection algorithms named Flower Pollination Algorithms (FPA) because of their propensity to accept less optimum solutions and avoid getting caught in local optimum solutions.The study analyses tweets from Kaggle first with the usual Term Frequency-Inverse Document Frequency statistical weighting filter and then with the FPA.Four baseline classifiers are used to train the features: Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and k-Nearest Neighbor (kNN).The results demonstrate that the FPA outperforms alternative feature subset selection algorithms.For the FPA, an average improvement in accuracy of 2.7% is seen.The SVM achieves a better accuracy of 98.99%.
Joint Multi-feature Learning for Facial Age Estimation

Age estimation from face images has attracted much attention due to its favorable of many real-world applications such as video surveillance and social networking. However, most existing studies usually directly extract aging-feature, which ignore the high age-related factors such as race and gender information. In this paper, we propose a joint multi-feature learning method for robust facial age estimation by extensively exploring age-related features. Specifically, we first specially learn the race and gender features from face images, which are two highly related information for age estimation of an individual. Then, we jointly learn the aging-feature by concatenating these race-specific and gender-specific information maps with the original face images. To fully utilize the continuity and the order of age labels, we form a regression-ranking age estimator to predict the final age. Experimental results on three benchmark databases demonstrate the superior performance of our proposed method on facial age estimation in comparison with other state-of-the-art methods.
CodPy: A Python Library for Machine Learning, Mathematical Finance, and Statistics

This monograph presents the algorithms that are implemented in the Python library CodPy—an acronym that stands for the “Curse Of Dimensionality in PYthon”. This library provides the user with a support vector machine (SVM) and encompasses a broad set of applications. The proposed algorithms apply to partial differential equations arising, for instance, in mathematical finance and fluid dynamics, as well as to discrete models arising in machine learning and statistics. Our basic numerical strategy relies on a combination of ideas from the theory of reproducing kernel Hilbert spaces (RKHS) and the theory of optimal transport. The authors have developed this library over the past decade, originally for applications in mathematical finance, and it has now reach a stage where it can be applied to problems of interest to engineering and industry.
DbAPE: Denoising-Based APE System for Improving English-Myanmar NMT

Automatic post-editing (APE) research aims to investigate methods for correcting systematic errors in machine translation (MT) results. Recent work has shown successful practices of APE for improving MT output quality; however, their effectiveness strongly relies on the availability of large-scale human-created APE triplets. The high production cost of human post-edited data has led to the absence of APE triplets for most language pairs, including English-Myanmar, which has become a limiting factor for the applicability of the APE task. This work investigates how to conduct the APE task on the English-Myanmar MT where human-edited APE triplets are unavailable. We build an APE system using only the monolingual and parallel MT corpora. The system takes the source sentence (<i>src</i>) and the MT output (<i>mt</i>) as inputs and produces the <i>post-edited mt</i> as output by operating the three processes together, including word alignment extraction, enriching <i>mt</i> using the extracted word alignment information, and denoising the enriched-version of <i>mt</i>. We conduct extensive experiments by applying our APE system as a post-processor to the raw output of the existing English-Myanmar MT systems and show that it significantly improves the quality of baseline translation results in terms of TER and BLEU scores. In addition, we perform word alignment experiments with four types of alignment methods and demonstrate that the proposed multilingual word aligner can achieve robust performance over previous state-of-the-art models.
‘Dubro’ Resophonic Guitar: Glissando Gestures

Whether in the Hawaiian, Bluegrass, Rock ‘n’ roll, film sound track or animated cartoon genre, the swoop (glissando) sound made on a slide-guitar is one the most instantly recognizable in western music. This paper reports on the complex acoustical and perceptual glissando of the opening few seconds of Warner Brothers ‘Looney Tunes’ ascending glissando, and its counterpart (descending glissando), both played on a ‘Dubro’ resophonic guitar. The aim is to analyze these guitar themes in an attempt to provide both a historical development, as well as a technical understanding of the generated sound. With the resophonic guitar tuned to open G (D-G-D-G-B-D), the radiated sounds, includes the guitarist gestures and the glissando sound of steel and glass bottleneck, Using the toolbox within Audacity software (time-domain, standard autocorrelation, spectrogram and noise reduction), the recorded tracks are transcribed for tempo, consonant, dissonant, string squeaks, and incoherent/coherent noise. This study also attempts to map the complex psychoacoustic tonal quality of a resophonic guitar, which has been demonstrated to impact emotionally on the listener. It is found that dynamic slide movement divides the string scale length into two coupled longitudinal vibrating segments, each producing a coherent continuous mirrored exponential varying pitch that extends to the guitar brilliance region (4.5–20 kHz). Incoherent or ‘hiss-like’ noise is found within the lower psychoacoustic warm region (0–0.5 kHz). This incoherent noise is linked to a slip-stick friction process between the slide and string. Slide material and slide direction varies the intensity of the noise that has a Voss-Clarke 1/f-like response with a Brownian ~ − 7 dB/10 Hz roll-off. It is proposed that the guitarists fretting arm musculoskeletal system plays a role in the generation incoherent or hiss-like noise.
Modular StoryGAN with Background and Theme Awareness for Story Visualization

Story visualization is a novel topic that intersects computer vision and natural language processing. In this task, given a series of natural language sentences that compose a story, a sequence of images should be generated that correspond to the sentences. Prior works have introduced recurrent generative models which outperform text-to-image models on this task; however, local and global consistency is a challenging attribute of these solutions. For the improvement, we proposed a new modular model architecture named Modular StoryGAN containing the best promising components of prior works. To measure the local and global consistency we introduced background and theme awareness, which are expected attributes of the solutions. Based on the human evaluation, the generated images demonstrate that Modular StoryGAN possesses background and theme awareness. Besides the subjective evaluation, the objective one also shows that our model outperforms the state-of-the-art CP-CSV and DuCo models.
Learning Adaptive Axis Attentions in Fine-tuning: Beyond Fixed Sparse Attention Patterns

We present a comprehensive study of sparse attention patterns in Transformer models.We first question the need for pre-training with sparse attention and present experiments showing that an efficient fine-tuning only approach yields a slightly worse but still competitive model.Then we compare the widely used local attention pattern and the less-well-studied global attention pattern, demonstrating that global patterns have several unique advantages.We also demonstrate that a flexible approach to attention, with different patterns across different layers of the model, is beneficial for some tasks.Drawing on this insight, we propose a novel Adaptive Axis Attention method, which learns-during fine-tuning-different attention patterns for each Transformer layer depending on the downstream task.Rather than choosing a fixed attention pattern, the adaptive axis attention method identifies important tokens-for each task and model layer-and focuses attention on those.It does not require pre-training to accommodate the sparse patterns and demonstrates competitive and sometimes better performance against fixed sparse attention patterns that require resource-intensive pre-training.
DialDoc 2022 Shared Task: Open-Book Document-grounded Dialogue Modeling

The paper presents the results of the Shared Task hosted by the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering co-located at ACL 2022.The primary goal of this Shared Task is to build goal-oriented informationseeking conversation systems that are grounded in the domain documents, where each dialogue could correspond to multiple sub-goals that are based on different documents.The task is to generate agent responses in natural language given the dialogue and document contexts.There are two task settings and leaderboards based on (1) the same sets of domains (SEEN) and ( 2 ) one unseen domain (UNSEEN).There are over 20 teams participating in Dev Phase and 8 teams participating in both Dev and Test Phases.There are multiple submissions that significantly outperform the baseline.The best-performing system achieves 52.06 F1 and the total of 191.30 on the SEEN task; and 34.65 F1 and the total of 130.79 on the UN-SEEN task.
Using Machine Learning Techniques to Support the Emergency Department

This research lays down foundations for a stronger presence of machine learning in the emergency department. Using machine learning to make predictions on a patient's situation can increase patient's health and decrease the waiting time. This paper explores to what extent it is possible to accurately predict ER outcome. These predictions will be based on routinely available ER data from a Dutch hospital. The data set used is representative for any Dutch Hospital. Prediction performance is compared between ML predictors. Using random forest and stacked ensemble gathered the best results. This research found that for more than half of the adult patients, the algorithm can very accurately predict hospitalization, with similar results for children and during the COVID-19. Moreover, it is investigated which characteristics and events contribute to the direction of the patient. Finally, several plans are introduced to substantially improve the ER process, for example by quickly reviewing patients selected by the algorithms. These might lead to an ER process that is significantly quicker, with more accurate diagnosis.