Reviewerly/exHarmony huggingface dataset:
BN for More Robust Estimation

As illustrated in previous sections, BN introduces inconsistent normalization operations during training (using mini-batch statistics, as shown in Eq. 3.5) and inference (using population statistics estimated in Eq. 3.6). This means that the upper layers are trained on representations different from those computed during inference. These differences become significant if the batch size is too small, since the estimates of the mean and variance become less accurate. This leads to significantly degenerated performance [1–4]. To address this problem, some normalization methods avoid normalizing along the batch dimension, as introduced in previous sections. Here, we will discuss the more robust estimation methods that also address this problem of BN. One way to reduce the discrepancy between training and inference is to combine the estimated population statistics for normalization during training.
A Novel Splitting Criterion Inspired by Geometric Mean Metric Learning for Decision Tree

Decision tree (DT) attracts persistent research attention due to its impressive empirical performance and interpretability in numerous applications. However, the growth of traditional yet widely-used univariate decision trees (UDTs) is quite time-consuming as they need to traverse all the features to find the splitting value with the maximal reduction of the impurity at each internal node. In this paper, we newly design a splitting criterion to speed up the growth. The criterion is induced from Geometric Mean Metric Learning (GMML) and then optimized under its diagonalized metric matrix constraint, consequently, a closed-form rank of feature discriminant abilities can at once be obtained and the top 1 feature at each node used to grow an intent DT (called as dGMML-DT, where d is an abbreviation for diagonalization). We evaluated the performance of the proposed methods and their corresponding ensembles on benchmark datasets. The experiment shows that dGMML-DT achieves comparable or better classification results more efficiently than the UDTs with 10x average speedup. Furthermore, dGMML-DT can straightforwardly be extended to its multivariable counterpart (dGMML-MDT) without needing laborious operations.
Unsupervised Image Translation with GAN Prior

Unsupervised image translation aims to learn the translation between two domains without paired data. Although impressive progress has been made in recent years, existing methods are difficult to build mapping between domains with drastic visual discrepancies. In this paper, we propose a novel framework for unsupervised image translation with large discrepancies. The key insight is to leverage the generative prior from pretrained GANs for the target domain (e.g., StyleGAN), to learn rich prior information of the target domain. We propose a two-stage framework, the GAN of the target domain is pretrained to obtain the prior information in the first stage, the pretrained GAN for the target domain is embedded as the decoder of the translation network and the translation network is trained with the guidance of the prior information in the second stage. Experimental results show the superiority of our method compared to the existing state-of-the-art methods for the translation between challenging and distant domains.
Hybrid Model for Named Entity Recognition

Named entity recognition is an important factor that has a direct and significant impact on the quality of neural sequence labelling. It entails choosing encoding input data to create grammatical and semantic representation vectors. The main goal of this research is to provide a hybrid neural network model for a specific sequence labelling task such as named entity recognition. Three subnetworks are used in this hybrid model to ensure that information at the character, capitalization levels, and word-level contextual representation is fully utilized. The authors used different samples for training and development sets on the CoNLL-2003 dataset to show that the model could compare its performance to that of other state-of-the-art models.
Detection of Dyslexic Children Using Machine Learning and Multimodal Hindi Language Eye-Gaze-Assisted Learning System

Children with dyslexia need specific instructions for spelling and word analysis from an early age. It is important to provide appropriate tools using technology for writing aids to such children that can help them to input text, while providing multiple feedback. However, it is unclear how children with dyslexia can efficiently use a gaze-based virtual keyboard (VK). In this study, we propose to use the typing performance of a multimodal Hindi language eye-gaze-assisted learning system based on a VK to help in the reduction of tracking errors for people with writing and reading deficiencies and to detect children with dyslexia. Performance was assessed at three levels: eye tracker, eye tracker with soft switch, and touchscreen as a baseline modality using a predefined copy-typing task. The system was validated through a series of experiments with 32 children (16 dyslexic and 16 control). The results show that the workload and the usability of the system are substantially different for children with dyslexia. Children with dyslexia have a lower typing performance when using the touchscreen modality or the eye tracker only. The detection of children with dyslexia from others was assessed with seven different types of classifiers using the typing speed on different words (AUC <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 0.9). These results highlight the need to have fully inclusive VKs. This work demonstrates the superior use of a multimodal system with participants having unique neuropsychological conditions and that the proposed system can be used to detect children with dyslexia.
Towards a Learner-Managed Education Credentialing System Based on Blockchain

Traditionally, a learner's education credentials are maintained by each educational institution. When individuals need to prove their education, they rely on their educational institutions to certify their education and learning records. This paper proposes a decentralized, learner-managed education credentialing system based on blockchain, where the learners' credentials are issued once, stored in a distributed system, and the learners have full control over how and who can access their credentials. The authors present the procedures for credential issuance, selective disclosure of an individual's credentials chosen by each learner, and credential verification by a third party. A proof-of-concept smart contract system has been developed to demonstrate the functionality of the proposed framework. The smart contracts are programmed using the Solidity programming language and tested on the Remix IDE. The authors present this simulation of smart contracts to handle entity registration, credential information storage, credential verification, and educational financial transactions.
Exploring Educational Applicability of Virtual Touch System on Maker Space

Interaction with gestures is more intuitive than traditional input with a keyboard and a mouse. It has gradually become the major technology for extended reality. However, for most users, gesture control is not familiar as other interactive devices, and most user interfaces of daily-use applications are currently not designed for gesture control. Thus, we propose a mid-air hand gesture control system, named vTouch to realize a touch device trained by deep learning with a convolutional neural network for image feature extraction, attention mechanisms for extracting time-series data features, and fully connected layer for classification. Besides, there is a customized driver in kernel mode which treats the vTouch system as a virtual multi-touch device. An experimental design is also proposed to evaluate the effectiveness of the training and the usability of the system.
Analysis of Various Machine Learning Techniques used for Automatic Text Summarization

The growing availability of documents has necessitated extensive research in the field of Natural Language Processing (NLP) for Automatic Text Summarization (ATS). Researchers have been striving to develop approaches for creating summaries since the 1950s, such that the machine-generated summary matches the human-made summary. Because manual text summarizing is a time-consuming and inherently tedious activity, automating it is expanding in popularity and so serves as a powerful motivator for academic study. This paper presents taxonomy for ATS and discusses about various approaches that are used for summarization of text. The paper outlines a basic structure of a generic framework for ATS. Various datasets that are frequently used in this domain is also provided. Comparative analysis of various prominent text summarizers on the basis of technique used, language of text, document source and summarization domain is also provided. Various issues faced by the researchers and future prospects are also discussed.
Research on Traffic Sign Recognition in Night Environment based on Deep Learning

In order to decrease in the accuracy of traffic sign recognition due to dim light at night, a novel YOLOv5 algorithm is proposed in this paper, which adds the Improved Adaptive Histogram Equalization (IAHE) method to image pre-processing, adjusts image brightness and contrast to highlight the related information about traffic signs. In view of the higher requirements of driving assistance system on the processing speed of recognition model and the standard convolution mode of backbone network is improved to the depth separable convolution method for model lightweight processing. To address the problem of wrong and missed detection generated by prediction box suppression, the conventional NMS is replaced by Weighted Boxes Fusion WBF to generate prediction box. Experiments have demonstrated that the improved algorithm has improved detection accuracy and decreased processing time for a single image compared with the original YOLOv5 algorithm in the self-built night environment dataset.
A QoE Prediction Model Combining Network Parameters and Video Quality

The advent of the 5G era and the theater performing arts market woes caused by Corona Virus Disease 2019 (COVID- 2019) epidemic have accelerated the emergence and growth of the cloud performing arts business. To improve the quality of service for cloud performing arts and live performances, it is critical to develop a predictive model that accurately and timely reflects the Quality of Experience (QoE). In this paper, we first filter three of the seven recognized application layer Quality of Service (QoS) parameters that represent the input network quality in this QoE prediction model. Then one of the four different video quality evaluation methods is selected as the most effective method to represent the video quality. The purpose of combining network quality and video quality is to build a more accurate and effective QoE prediction model.
Public Speaking Simulator with Speech and Audience Feedback

Public speaking is one of the most important ways to share ideas with many people in different domains such as education, training, marketing, or healthcare. Being able to master this skill allows the speaker to clearly advocate for their subject and greatly influence others. However, most of the population reported having public speaking anxiety or glossophobia, which prevents them from effectively conveying their messages to others. One of the best solutions is to have a safe and private space to practice speaking in front of others. As a result, this research work is proposed with the overarching goal of providing people with virtual environments to practice in front of simulated audiences. In addition, the proposed work will aim to have live audience feedback and speech analysis details which could be useful for the users. The experiments via a user study provide insights into the proposed public speaking simulator.
Prediphant: Short Term Heavy User Prediction

Traffic prediction is of paramount importance for the correct management of network infrastructures. Most research efforts try to forecast the aggregated traffic over the network and over large time windows. In this work, we tackle the problem the other way around. That is, we predict the behaviour of individual users over short time windows. First, we investigate the contribution of the most data eager users to the global network traffic. We do it by analyzing network traces coming from several thousand real users. Then, we design a technique, based on a combination of Natural Language Processing (NLP) and Long Short-Term Memory (LSTM) machine learning models, that leverages past navigation patterns to predict sudden changes in the amount of resources consumed by each user. Finally, we evaluate our method using real data finding it is able to predict about 80% of the users that will rump up their network needs in most realistic scenarios.
The 3-dimension integer sub-decomposition method for Edwards curve cryptography

In this work, another version of the integer sub-decomposition (ISD) method is presented to compute a scalar multiplication sP on Edwards curves Eds which are defined over prime fields Fp. This version depended on applying a 3- dimension of the ISD generators. The elements of these generators are chosen randomly from the range [1, p-1], where p is a prime number. In each vector, the elements are relatively prime to each other. Using these generators, a scalar s can be decomposed into s1 and s2 . These scalars are sub-decomposed again into sub-scalars s11, s12 and s 21, s 22 . The scalar multiplication sP using the 3-ISD method is computed using the sub-scalars and the efficiently computable endomorphisms of Edwards curve Ed defined over Fp. On the 3-ISD method, fast computations are determined based on the randomization choices of the elements that form the 3-ISD generators in comparison with the previous version that is depended on the 2-ISD generators. In comparison with the 2-ISD computation method to compute sP, the 3-ISD method considers as more secure communications using the Edwards curve cryptography.
Line-of-sight Estimation for Missile with Censored Measurement

In this paper, we focus on the Line-of-sight(LOS) information estimation for missiles with censored multi-dimensional measurement. To this end, we first construct the nonlinear dynamic relative model between the missile and the target in 3D space to describe the correlated LOS information. Then, we build the measurement model including the censored measurement, which results from view limit constraints and uncensored measurement obtained from the geometric relationship or radars. Next, an Improved Tobit Kalman filter is proposed to realize the measurement with two censoring limits and then the Improved Tobit Kalman filter is combined with the fifth-degree cubature Kalman filter to handle the nonlinearity of the dynamic model. Finally, numerical simulations are given to demonstrate the effectiveness of our proposed algorithm.
A Comprehensive Survey and Outlook for Cross-Resolution Person Re-Identification

Person re-identification (Re-ID) is a fundamental task in computer vision which has achieved significant progress in recent years. However, the existing promising algorithms are typically based on the assumption that all the images have the same and sufficiently high resolution (HR), ignoring the fact that the images are often captured with different resolutions. This study intends to present a comprehensive overview of cross-resolution (CR) person Re-ID to promote a deeper understanding of this topic and further research. We first group the current techniques into three categories: dictionary-learning-based, super-resolution-based, and generative-adversarial-network-based methods. The motivation, principles, benefits, and drawbacks of these techniques are extensively discussed. Then, the ways to construct synthetic multi-low-resolution (MLR) datasets and the performance comparisons of the state-of-the-art algorithms on five MLR datasets are demonstrated. Finally, challenges and potential research directions are further discussed.
Efficient Private Set Intersection Cardinality Protocol in the Reverse Unbalanced Setting

Private set intersection cardinality (PSI-CA) is a variant of private set intersection (PSI) that allows two parties, the sender and the receiver, to compute the cardinality of the intersection without leaking anything more to the other party. It’s one of the best-studied applications of secure computation, and many PSI-CA protocols in balanced or unbalanced scenarios have been proposed. Generally, unbalanced scenario means that the private set size of the receiver is significantly smaller than that of the sender. This paper mainly focuses on a new scenario in which the receiver’s set size (client) is much larger than that of the sender (server) called the reverse unbalanced scenario. We study PSI-CA protocols that are secure against semi-honest adversaries, using the Hash-Prefix filter to effectively reduce the computation and communication overhead. We greatly optimize the previous unbalanced PSI-CA protocol and construct a reverse unbalanced PSI-CA protocol. In addition, we introduce private information retrieval (PIR) to resist the privacy leakage of the Hash-Prefix filter. By implementing all protocols on the same platform, we compare the protocols’ performance theoretically and experimentally. Combined with the Cuckoo filter, elliptic curve and multi-threading, the computational and communication efficiency of our protocol is 26.87 $$\times $$ and 8.48 $$\times $$ higher than the existing unbalanced PSI-CA protocols. By setting sets with significant differences in size, we also prove the feasibility of our protocol in anonymous identity authentication.
Word Order is Considerable: Contextual Position-aware Graph Neural Network for Text Classification

Text Classification is a crucial and classical research branch in Natural Language Processing. Recently, Graph Neural Networks (GNNs) have been explored for this task since GNNs are proficient in handling non-Euclidean data with more complex relations. Despite the success, these graph-based methods have some limitations to model the word order information in the document. In this paper, to address this issue, we propose the Contextual Position-aware Graph Neural Network for text classification, which includes the Position-aware Graph Attention module and the Contextual Fusion module. The former can respectively capture left-side, self-side, and right-side word order information of each word. The latter enables the Context-fused Graph LSTM to learn word contextual representations by controlling three contextual information flows. In addition, experimental results have demonstrated that our proposed method can effectively enhance text representations, thus improving the classification performance.
VidHarm: A Clip Based Dataset for Harmful Content Detection

Automatically identifying harmful content in video is an important task with a wide range of applications. However, there is a lack of professionally labeled open datasets available. In this work VidHarm, an open dataset of 3589 video clips from film trailers annotated by professionals, is presented. An analysis of the dataset is performed, revealing among other things the relation between clip and trailer level annotations. Audiovisual models are trained on the dataset and an in-depth study of modeling choices conducted. The results show that performance is greatly improved by combining the visual and audio modality, pre-training on large-scale video recognition datasets, and class balanced sampling. Lastly, biases of the trained models are investigated using discrimination probing.VidHarm is openly available, and further details are available at the webpage https://vidharm.github.io/
Comparing Peircean Algorithm with Various Bio-inspired Techniques for Multi-dimensional Function Optimization

Peirce’s theory of evolution gives a highly efficient optimiza- tion algorithm in the domain of evolutionary computation (EC). Peircean Evolutionary Algorithm (P-EA) has the potential to solve the existing drawbacks of classical Evolutionary Algorithms such as loss of diversity, stagnation, or premature convergence. In this work, we compare P-EA with other state- of-the-art algorithms on a set of benchmark mathematical functions that are widely used to gauge their performance. These algorithms are already tested on several mathematical functions, but never have been compared with P-EA. The experimental results show that P-EA outperforms on complex functions like Michalewicz and Rastrigin with higher number of dimensions. These results help to improve the viability of P-EA for EC community.
Nesting Forward Automatic Differentiation for Memory-Efficient Deep Neural Network Training

An activation function is an element-wise mathematical function and plays a crucial role in deep neural networks (DNN). Many novel and sophisticated activation functions have been proposed to improve the DNN accuracy but also consume massive memory in the training process with back-propagation. In this study, we propose the nested forward automatic differentiation (Forward-AD), specifically for the element-wise activation function for memory-efficient DNN training. We deploy nested Forward-AD in two widely-used deep learning frameworks, TensorFlow and PyTorch, which support the static and dynamic computation graph, respectively. Our evaluation shows that nested Forward-AD reduces the memory footprint by up to 1.97× than the baseline model and outperforms the recomputation by 20% under the same memory reduction ratio.
Conditions for the implementation of the digital transformation of the Russian educational system

Most of the modern scientific researches of foreign scientists consider the consequences after the digital transformation of the educational environment, characterize the process of work of various adaptation mechanisms, and also explore the relationship of experts, teachers, students to the new digital environment. The purpose of the study is to determine the internal and external factors thanks to which the digital transformation of the educational system in Russia takes place. The authors have built a model for transforming the educational system of Russia on the basis of integrating a complex of digital technologies into it. The methodological basis of the study is based on the principles of the systematic approach, within which the process of digital transformation is considered from the standpoint of systemic transformations, the application of which has a complex effect on the development of society. The nature of digital renewal as a single transformation, as a mechanism for integrating digital technologies that can qualitatively improve the results of the educational process is substantiated. The results of digital transformation in the course of the study were examined from various perspectives. The model of digital implementation of digital transformation developed by the authors provides an opportunity to predict trends in the development of the educational process in Russia, which ultimately will allow to develop and adopt effective strategies. The main role of modern digital technologies lies in the technological updating of the current educational process in the country, expanding the range of educational programs and technologies, as well as qualitatively improving the motivational level of the student and the teacher.