Reviewerly/exHarmony huggingface dataset:
Use of modern technologies and digital tools in the context of distance and mixed learning

Widespread use of distance learning, which is equated with conventional education, is recognised as one of the priority tasks of Ukrainian education, which implements information communication and digital technologies. The relevance of the study increases dramatically due to the fact that in the context of the spread of coronavirus and the real risk of infection growing with it, almost all educational institutions are switching to distance learning mode. The key figure in solving this problem is the qualification of the teacher and their education, the level of which greatly influences the effectiveness of the entire process of distance and mixed learning. The purpose of the study was to investigate information technologies and digital tools that could be used in educational institutions in the context of distance and mixed learning. As a result of the study, the authors were able to analyse world practice of using technologies and information resources for distance and mixed forms of learning.
A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

At present, iris recognition has been widely used as a biometrics-based security enhancement technology. However, in some application scenarios where a long-distance camera is used, due to the limitations of equipment and environment, the collected iris images cannot achieve the ideal image quality for recognition. To solve this problem, we proposed a modified sparrow search algorithm (SSA) called chaotic pareto sparrow search algorithm (CPSSA) in this paper. First, fractional-order chaos is introduced to enhance the diversity of the population of sparrows. Second, we introduce the Pareto distribution to modify the positions of finders and scroungers in the SSA. These can not only ensure global convergence, but also effectively avoid the local optimum issue. Third, based on the traditional contrast limited adaptive histogram equalization (CLAHE) method, CPSSA is used to find the best clipping limit value to limit the contrast. The standard deviation, edge content, and entropy are introduced into the fitness function to evaluate the enhancement effect of the iris image. The clipping values vary with the pictures, which can produce a better enhancement effect. The simulation results based on the 12 benchmark functions show that the proposed CPSSA is superior to the traditional SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC). Finally, CPSSA is applied to enhance the long-distance iris images to demonstrate its robustness. Experiment results show that CPSSA is more efficient for practical engineering applications. It can significantly improve the image contrast, enrich the image details, and improve the accuracy of iris recognition.
potential of online learning in modern conditions and its use at different levels of education

The purpose of the work is to explore the potential of online learning in modern conditions and to assess the possibilities of its use at different stages of the educational process. The reason for the mass appeal to this form of education is that the entire education system from primary to higher level underwent a transformation during the period of isolation associated with the spread of COVID-19. Practice has shown that in the current conditions, the existing resources of educational institutions can effectively transform formal education into online education with the help of virtual classes and other key online tools. Today, taking into account the unstable epidemiological situation in the world, online training continues to be relevant for various educational organizations and is used today along with the traditional form of training. For this reason, the study of the potential of this form of education will not lose its relevance for a long time. The use of online learning technologies places high demands on teachers.
Cryptocurrencies activity as a complex network: Analysis of transactions graphs

Abstract The number of users approaching the world of cryptocurrencies exploded in the last years, and consequently the daily interactions on their underlying distributed ledgers have intensified. In this paper, we analyze the flow of these digital transactions in a certain period of time, trying to discover important insights on the typical use of these technologies by studying, through complex network theory, the patterns of interactions in four prominent and different Distributed Ledger Technologies (DLTs), namely Bitcoin, DogeCoin, Ethereum, Ripple. In particular, we describe the Distributed Ledger Network Analyzer (DiLeNA), a software tool for the investigation of the transactions network recorded in DLTs. We show that studying the network characteristics and peculiarities is of paramount importance, in order to understand how users interact in the DLT. For instance, our analyses reveal that all transaction graphs exhibit small world properties.
Visual Co-Occurrence Alignment Learning for Weakly-Supervised Video Moment Retrieval

Video moment retrieval aims to localize the most relevant video moment given the text query. Weakly supervised approaches leverage video-text pairs only for training, without temporal annotations. Most current methods align the proposed video moment and the text in a joint embedding space. However, in lack of temporal annotations, the semantic gap between these two modalities makes it predominant to learn joint feature representation for most methods, with less emphasis on learning visual feature representation. This paper aims to improve the visual feature representation with supervisions in the visual domain, obtaining discriminative visual features for cross-modal learning. Based on the observation that relevant video moments (i.e., share similar activities) from different videos are commonly described by similar sentences; hence the visual features of these relevant video moments should also be similar despite that they come from different videos. Therefore, to obtain more discriminative and robust visual features for video moment retrieval, we propose to align the visual features of relevant video moments from different videos that co-occurred in the same training batch. Besides, a contrastive learning approach is introduced for learning the moment-level alignment of these videos. Through extensive experiments, we demonstrate that the proposed visual co-occurrence alignment learning method outperforms the cross-modal alignment learning counterpart and achieves promising results for video moment retrieval.
Machine Reading Comprehension as Data Augmentation: A Case Study on Implicit Event Argument Extraction

Implicit event argument extraction (EAE) is a crucial document-level information extraction task that aims to identify event arguments beyond the sentence level.Despite many efforts for this task, the lack of enough training data has long impeded the study.In this paper, we take a new perspective to address the data sparsity issue faced by implicit EAE, by bridging the task with machine reading comprehension (MRC).Particularly, we devise two data augmentation regimes via MRC, including: 1) implicit knowledge transfer, which enables knowledge transfer from other tasks, by building a unified training framework in the MRC formulation, and 2) explicit data augmentation, which can explicitly generate new training examples, by treating MRC models as an annotator.The extensive experiments have justified the effectiveness of our approach -it not only obtains state-of-the-art performance on two benchmarks, but also demonstrates superior results in a data-low scenario.
Theory and Practice of VR/AR in K-12 Science Education—A Systematic Review

Effective teaching of science requires not only a broad spectrum of knowledge, but also the ability to attract students’ attention and stimulate their learning interest. Since the beginning of 21st century, VR/AR have been increasingly used in education to promote student learning and improve their motivation. This paper presents the results of a systematic review of 61 empirical studies that used VR/AR to improve K-12 science teaching or learning. Major findings included that there has been a growing number of research projects on VR/AR integration in K-12 science education, but studies pinpointed the technical affordances rather than the deep integration of AR/VR with science subject content. Also, while inquiry-based learning was most frequently adopted in reviewed studies, students were mainly guided to acquire scientific knowledge, instead of cultivating more advanced cognitive skills, such as critical thinking. Moreover, there were more low-end technologies used than high-end ones, demanding more affordable yet advanced solutions. Finally, the use of theoretical framework was not only diverse but also inconsistent, indicating a need to ground VR/AR-based science instruction upon solid theoretical paradigms that cater to this particular context.
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks

This paper studies continual learning (CL) of a sequence of aspect sentiment classification (ASC) tasks in a particular CL setting called domain incremental learning (DIL).Each task is from a different domain or product.The DIL setting is particularly suited to ASC because in testing the system needs not know the task/domain to which the test data belongs.To our knowledge, this setting has not been studied before for ASC.This paper proposes a novel model called CLASSIC.The key novelty is a contrastive continual learning method that enables both knowledge transfer across tasks and knowledge distillation from old tasks to the new task, which eliminates the need for task ids in testing.Experimental results show the high effectiveness of CLASSIC.
Deep learning—a first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact

Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Similar to the basic structure of a brain, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term 'deep learning', and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources (image, language, medical, mixed). In addition, we review the common architectures, methods, pros, cons, evaluations, challenges and future directions for every sub-category.
Weibo Text Sentiment Analysis Based on BERT and Deep Learning

With the rapid increase of public opinion data, the technology of Weibo text sentiment analysis plays a more and more significant role in monitoring network public opinion. Due to the sparseness and high-dimensionality of text data and the complex semantics of natural language, sentiment analysis tasks face tremendous challenges. To solve the above problems, this paper proposes a new model based on BERT and deep learning for Weibo text sentiment analysis. Specifically, first using BERT to represent the text with dynamic word vectors and using the processed sentiment dictionary to enhance the sentiment features of the vectors; then adopting the BiLSTM to extract the contextual features of the text, the processed vector representation is weighted by the attention mechanism. After weighting, using the CNN to extract the important local sentiment features in the text, finally the processed sentiment feature representation is classified. A comparative experiment was conducted on the Weibo text dataset collected during the COVID-19 epidemic; the results showed that the performance of the proposed model was significantly improved compared with other similar models.
Deep Learning for Automatic Violence Detection: Tests on the AIRTLab Dataset

Following the growing availability of video surveillance cameras and the need for techniques to automatically identify events in video footages, there is an increasing interest towards automatic violence detection in videos. Deep learning-based architectures, such as 3D Convolutional Neural Networks, demonstrated their capability of extracting spatio-temporal features from videos, being effective in violence detection. However, friendly behaviours or fast moves such as hugs, small hits, claps, high fives, etc., can still cause false positives, interpreting a harmless action as violent. To this end, we present three deep-learning based models for violence detection and test them on the AIRTLab dataset, a novel dataset designed to check the robustness of algorithms against false positives. The objective is twofold: on one hand, we compute accuracy metrics on the three proposed models (two are based on transfer learning and one is trained from scratch), building a baseline of metrics for the AIRTLab dataset; on the other hand, we validate the capability of the proposed dataset of challenging the robustness to false positives. The results of the proposed models are in line with the scientific literature, in terms of accuracy, with transfer learning-based networks exhibiting better generalization capabilities than the trained from scratch network. Moreover, the tests highlighted that most of the classification errors concern the identification of non-violent clips, validating the design of the proposed dataset. Finally, to demonstrate the significance of the proposed models, the paper presents a comparison with the related literature, as well as with models based on well-established pre-trained 2D Convolutional Neural Networks 2D CNNs. Such comparison highlights that 3D models get better accuracy performance than time distributed 2D CNNs (merged with a recurrent model) in processing the spatio-temporal features of video clips. The source code of the experiments and the AIRTLab dataset are available in public repositories.
Blockchain and IoT Technology in Transformation of Education Sector

Technology has impacted every field of life, and Education sector is no exception. It has seen seismic changes off-late. The traditional classroom teaching-learning process involved teachers and students using the black-board approach. But, the past years have seen a change in dynamics of this process. Technology has crept in and has profoundly changed it for better. On one hand, when traditional classrooms had teachers, students and books as players, the current classroom has teachers, students, robots, e-books, laptops and books as the players. Today’s classrooms have enabled the teaching-learning process cross boundaries in the form of e-lectures and tutorials. Massive amount of Information is readily available for the process. Also, a shift from “Teacher-centric” to “Student Centric” can be seen in the current scenario. Several technologies have been key-players in revolutionizing the education sector, but the focus of this paper is concentrated in exploiting the applications of Blockchain and IoT in the sector and identifying the areas where they prove to be beneficial. An extensive literature survey shall be done to understand, and identify how these technologies are a solution to major educational problems. The teaching learning process needs to be understood with respect to various parameters: change in process, change in players, change in outcomes derived, and others. The associated challenges shall also be identified. A detailed analysis of these enables us to conclude that these technologies, can revolutionize the education sector for better in future, particularly Blockchain is young, but, owing to the benefits derived from it, further research and adoption shall for surely change the teaching-learning process.
SAA-Net: U-shaped network with Scale-Axis-Attention for liver tumor segmentation

In liver tumor segmentation tasks, the problems of multi-scale and global spatial modeling significantly affect the segmentation accuracy. For multi-scale feature extraction, we propose a dynamic scale attention mechanism, which assigns adaptive weights to multi-scale convolutions. Scale Attention could fuse receptive fields from multiple scales, which is beneficial to segmentation of multi-scale targets. For global modeling of spatial information, Axis Attention is proposed, which optimizes the computational resources utilization of self-attention and the attentive effect of convolution attention simultaneously. Axis Attention could model spatial long-range dependencies effectively and efficiently. Scale Attention and Axis Attention are organically combined with a style of adaptive global pooling and the composite proposed mechanism is called Scale-Axis-Attention (SAA). We incorporate it into U-shaped network to improve the performance of liver tumor segmentation, termed as SAA-Net. Our method not only is far superior to self-attention in terms of the computational resources utilization, but also incorporates the scale and spatial attention mechanisms simultaneously for performance improvement. We show that SAA-Net achieves the improved model capability and generalization performance through extensive experiments on qualitative and quantitative test datasets. Experiments also demonstrate the effectiveness of our method in the segmentation of tumors with small size.
One-Shot Talking Face Generation from Single-Speaker Audio-Visual Correlation Learning

Audio-driven one-shot talking face generation methods are usually trained on video resources of various persons. However, their created videos often suffer unnatural mouth shapes and asynchronous lips because those methods struggle to learn a consistent speech style from different speakers. We observe that it would be much easier to learn a consistent speech style from a specific speaker, which leads to authentic mouth movements. Hence, we propose a novel one-shot talking face generation framework by exploring consistent correlations between audio and visual motions from a specific speaker and then transferring audio-driven motion fields to a reference image. Specifically, we develop an Audio-Visual Correlation Transformer (AVCT) that aims to infer talking motions represented by keypoint based dense motion fields from an input audio. In particular, considering audio may come from different identities in deployment, we incorporate phonemes to represent audio signals. In this manner, our AVCT can inherently generalize to audio spoken by other identities. Moreover, as face keypoints are used to represent speakers, AVCT is agnostic against appearances of the training speaker, and thus allows us to manipulate face images of different identities readily. Considering different face shapes lead to different motions, a motion field transfer module is exploited to reduce the audio-driven dense motion field gap between the training identity and the one-shot reference. Once we obtained the dense motion field of the reference image, we employ an image renderer to generate its talking face videos from an audio clip. Thanks to our learned consistent speaking style, our method generates authentic mouth shapes and vivid movements. Extensive experiments demonstrate that our synthesized videos outperform the state-of-the-art in terms of visual quality and lip-sync.
A Robust Chaos-Based Technique for Medical Image Encryption

Transmission and storage of medical data using cloud-based Internet-of-health-systems (IoHS) necessitate important prerequisites, such as secrecy, legitimacy, and integrity. This paper recommends a novel hybrid encryption/decryption scheme that can be applied in e-healthcare, or IoHS, for the protection of medical images. The proposed system explores innovative perturbation algorithms that utilize novel chaotic maps. The proposed perturbation-based data encryption is employed in both rounds of confusion and diffusion to cope the drawbacks of traditional chaos-based confusion and diffusion architectures. Particularly, (i) the new maps parameters and chaotic sequences are used to control the permutation and diffusion properties of the scheme, and (2) the derived properties control pixel shuffling and operations of substitution. Different techniques and tests are used to analyzed the chaotic behaviors of the proposed system, including the bifurcation diagram, Lyapunov exponents, as well as the NIST and DIEHARD tests. Moreover, evaluation using various test images indicated that the proposed cryptosystem is fast, have high efficiency, showed high robustness and protection of medical images, and documented the good ability to withstand a variety of cyber-attacks. Furthermore, quantitative results using benchmark color and greyscale images prove the high security levels, sensitivity, and low residual intelligibility with high quality recovered data of our technique than several typical and state-of-the-art encryption schemes. This has been documented using statistical and security analysis metrics, such as number of pixels change rate (NPCR, 99.814&#x0025;), unified average changing intensity (UACI, 33.694&#x0025;), peak signal-to-noise-ratio (PSNR, 7.723), and Shannon entropy (7.998).
Towards Low Light Enhancement With RAW Images

In this paper, we make the first benchmark effort to elaborate on the superiority of using RAW images in the low light enhancement and develop a novel alternative route to utilize RAW images in a more flexible and practical way. Inspired by a full consideration on the typical image processing pipeline, we are inspired to develop a new evaluation framework, Factorized Enhancement Model (FEM), which decomposes the properties of RAW images into measurable factors and provides a tool for exploring how properties of RAW images affect the enhancement performance empirically. The empirical benchmark results show that the Linearity of data and Exposure Time recorded in meta-data play the most critical role, which brings distinct performance gains in various measures over the approaches taking the sRGB images as input. With the insights obtained from the benchmark results in mind, a RAW-guiding Exposure Enhancement Network (REENet) is developed, which makes trade-offs between the advantages and inaccessibility of RAW images in real applications in a way of using RAW images only in the training phase. REENet projects sRGB images into linear RAW domains to apply constraints with corresponding RAW images to reduce the difficulty of modeling training. After that, in the testing phase, our REENet does not rely on RAW images. Experimental results demonstrate not only the superiority of REENet to state-of-the-art sRGB-based methods and but also the effectiveness of the RAW guidance and all components.