Reviewerly/exHarmony huggingface dataset:
Color Image Restoration Using Sub-Image Based Low-Rank Tensor Completion

Many restoration methods use the low-rank constraint of high-dimensional image signals to recover corrupted images. These signals are usually represented by tensors, which can maintain their inherent relevance. The image of this simple tensor presentation has a certain low-rank property, but does not have a strong low-rank property. In order to enhance the low-rank property, we propose a novel method called sub-image based low-rank tensor completion (SLRTC) for image restoration. We first sample a color image to obtain sub-images, and adopt these sub-images instead of the original single image to form a tensor. Then we conduct the mode permutation on this tensor. Next, we exploit the tensor nuclear norm defined based on the tensor-singular value decomposition (t-SVD) to build the low-rank completion model. Finally, we perform the tensor-singular value thresholding (t-SVT) based the standard alternating direction method of multipliers (ADMM) algorithm to solve the aforementioned model. Experimental results have shown that compared with the state-of-the-art tensor completion techniques, the proposed method can provide superior results in terms of objective and subjective assessment.
Budget Deficits and Stock Market Performance in Emerging and Frontier Markets in Africa

Stock markets provide firms an important source to raise equity capital to finance investment.However, some macro indicators, such as budget deficits, affect these markets and can hinder their performance.In Africa, although the performance of the Stock Exchange has been affected by budget deficits from January 2000 to December 2017, the mechanisms of these relationships have not been studied.The purpose of this research was to examine the relationships between budget deficits and stock markets in Africa.The research questions addressed whether budget deficits had a significant effect on stock market performance.The data used for the study were obtained from the World Bank's World Development Indicators, Association of Security Exchange in Africa and International Monetary Fund financial statistics.The results indicate that a rise in budget deficits, holding other factors constant, negatively affects stock market performance.This might be due to the excessive borrowing of governments from the domestic market, which affects people's willingness to patronise the stock market and to lend to firms for long-term investments.Other factors, such as money supply and GDP growth rate, significantly improve stock market performance.The implication of these results is that the government should manage deficit financing prudently with regard to borrowing to support stock market activities in Africa.
Avoiding Overfitting: new algorithms to improve generalization in Convolutional Neural Networks

Deep Learning has achieved state-of-the-art results in several domains, such as im- age processing, natural language processing, and audio processing. To accomplish such results, it uses neural networks with several processing layers along with a massive amount of labeled information. One particular family of Deep Learning is the Convolutional Neural Networks (CNNs), which work using convolutional layers de- rived from the digital signal processing area, being very helpful to detect relevant features in unstructured data, such as audio and pictures. One way to improve results on CNN is to use regularization algorithms, which aim to make the training process harder but generate models that generalize better for inference when used in applications. The present work contributes to the area of regularization methods for CNNs, proposing more methods for use in different image processing tasks. This thesis presents a collection of works developed by the author during the research period, which were published or submitted until the present time, presenting: (i) a survey, listing recent regularization works and highlighting the solutions and problems of the area; (ii) a neuron dropping method to use in the tensors generated during CNNs training; (iii) a variation of the mentioned method, changing the dropping rules, targeting different features of the tensor; and (iv) a label regularization algorithm used in different image processing problems.
Exploring English as a Foreign Language (EFL) Teachers’ Experience of Flow During Online Classes

The present study explored Iranian English as a Foreign Language (EFL) teachers’ experience of flow in online teaching. To this aim, interviews were conducted to explore the conditions reported to facilitate or hinder the experience of flow among teachers. Twenty-four Iranian EFL teachers (eleven males and thirteen females) were selected for semi-structured interviews to collect qualitative data concerning their perceptions towards the online teaching-learning process. Participants had limited experience of online teaching Findings revealed that all teachers had experienced flow in online classes. It was also found that, besides previously used concepts which led to teacher flow, students’ digital literacy, teachers’ feeling of dominance on the subject, and peer communication were influential in enhancing teachers’ flow in online classes. Finally, the inability to judge learners’ physical and emotional condition, technical obstacles as well as limitations due to face-to-face interaction were reported to be the main obstacles hindering teachers’ perceived flow in online classes. Possible implications for teacher education programs are discussed.
MB-FL: Macro-Block Fuzzy Logic for Video Compression in Multimedia Applications

Methods to reduce the iterations needed to process a given video stream are referred to as video compression techniques.Videos consume a large amount of storage space on computational systems or handheld devices, and video compression is also used to lower the dimensionality of data owing to constraints on storage resources.In this study, we propose a macro block-based fuzzy logic video compression (MB-FL) algorithm.The proposed approach uses a fuzzy-based search to maintain pixel resolution, which is ideal for realtime streaming media and thus increases peak signal-to-noise ratio (PSNR) and subjective quality.Compression is performed on repeated frames of data files via complex equations, and the repeating patterns are then substituted with smaller data or coding fragments.Owing bandwidth limitations, compression is often necessary to transmit and receive content over network connections.Using a fuzzy membership function, the multiscale aspect of our method evaluates the connection of individual components in the current frame to those in the reference frame.The results of an experimental evaluation show that the proposed approach significantly compressed files using a fuzzy-based search.We compared the performance of MB-FL with that of existing models to measure the quality of compressed video stream.
Sarcasm Detection in Twitter - Performance Impact While Using Data Augmentation: Word Embeddings

Sarcasm is the use of words commonly used to ridicule someone or for humorous purposes.Several studies on sarcasm detection have utilized different learning algorithms.However, most of these learning models have always focused on the contents of expression only, thus leaving the contextual information in isolation.As a result, they failed to capture the contextual information in the sarcastic expression.Moreover, some datasets used in several studies have an unbalanced dataset, thus impacting the model result.In this paper, we propose a contextual model for sarcasm identification in Twitter using various pre-trained models and augmenting the dataset by applying Global Vector representation (GloVe) for the construction of word embedding and context learning to generate more sarcastic data, and also perform additional experiments by using the data duplication method.Data augmentation and duplication impact is tested in various datasets and augmentation sizes.In particular, we achieved the best performance after using the data augmentation method to increase 20% of the data labeled as sarcastic and improve the performance by 2.1% with an F1 Score of 40.44% compared to 38.34% before using data augmentation in the iSarcasm dataset.
Subscripto multiplex: A Riemannian symmetric positive definite strategy for offline signature verification

The human handwritten signature is considered to be a significant biometric trait. In the case of offline signatures, the problem is addressed as an image recognition task. On the other hand, the visual representation of symmetric positive definitive matrices, usually by means of the covariance descriptor of the image feature maps, forms a specific Riemannian manifold with a widespread usage and a favorable performance in a plethora of applications. Surprisingly, no records of offline-signature-verification-oriented research in the space of symmetric positive definitive matrix have been found up to now. In this work, we propose, for the first time in offline signature-verification literature, mapping of handwritten signature images in points of the tangent space of a connected symmetric positive definitive manifold for verification purposes. Furthermore, based on the principles of differential geometry, we address the notorious limited training problem of offline signature verification in this manifold by proposing two different feature augmentation methods. The efficiency of the proposed method is evaluated using three popular datasets of Western and Asian origin. Error rates against skilled and random forgery in both baselines as well augmentation scenarios are strong indicators of the informative and highly discriminative nature of symmetric positive definitive manifold oriented representation.
Socially assistive robots' deployment in healthcare settings: a global perspective

One of the major areas where social robots are finding their place in society is for healthcare-related applications. Yet, very little research has mapped the deployment of socially assistive robots (SARs) in real settings. Using a documentary research method, we were able to trace back 279 experiences of SARs deployments in hospitals, elderly care centers, occupational health centers, private homes, and educational institutions worldwide from 33 different countries, and involving 52 different robot models. We retrieved, analyzed, and classified the functions that SARs develop in these experiences, the areas in which they are deployed, the principal manufacturers, and the robot models that are being adopted. The functions we identified for SARs are entertainment, companionship, telepresence, edutainment, providing general and personalized information or advice, monitoring, promotion of physical exercise and rehabilitation, testing and pre-diagnosis, delivering supplies, patient registration, giving location indications, patient simulator, protective measure enforcement, medication and well-being adherence, translating and having conversations in multiple languages, psychological therapy, patrolling, interacting with digital devices, and disinfection. Our work provides an in-depth picture of the current state of the art of SARs' deployment in real scenarios for healthcare-related applications and contributes to understanding better the role of these machines in the healthcare sector.
ON THE RELATIONSHIP OF TYPES OF SOCIAL CREATIVITY WITH MORAL IDENTITY AND TOLERANCE

Background. Th e article presents the data of an empirical study of the relationship between tolerance, moral identity and social creativity. Th e defi nition of social creativity is given as the ability to generate original solutions to problems in social interaction. Th e classifi cation of social creativity according to the content of the task is proposed: prosocial and asocial (tasks with negative connotation) creativity Objective of the study was to identify the relationship between the types of social creative news with tolerance and moral identity. Sample. Th e study involved high school students and university and college students (N = 96). Methods. Th e methods of “Tolerance Index” (Soldatova et al.), moral identity (Aquino, Americus. II) and tasks for divergent thinking, as which situations with diff erent connotations were proposed — prosocial and negative. Results. Hypotheses about the positive relationship of prosocial creativity and the negative relationship of a creativity with tolerance and moral identity were tested. Th e hypotheses were partially confi rmed: a positive relationship of moral identity with antisocial creativity in a task with a prosocial connotation was revealed; a negative relationship of antisocial creativity in a revenge situation task with a moral and general tolerance index. Conclusion are drawn about the regulating and restraining function that tolerance and moral identity can perform in antisocial creativity: high values of parameters correlate with rejection of revenge, and vice versa, low values correlate with ideas of revenge, while the regulating role of moral identity may not be found in a prosocial situation. Th e ways of further study of social creativity are proposed and the importance for understanding the phenomenon of studying creativity in the case of respondents generating answers-refusals to off er solutions and answers of the opposite valence to the task is noted.
PROFESSIONAL IDENTITY AND THE SATISFACTION WITH THE PROFESSIONAL ACTIVITIES AS FACTORS OF WELL-BEING IN THE WORKPLACE

Background. Th e present paper discusses the relevance of identifying oneself with a professionalism, the attitude to the profession and the role in the professional activity as an integral characteristic of the professional identity among representatives of diff erent positions. Objective. Th e identifi cation of the relationship between the professional identity and satisfaction with professional activities (work process, working conditions, organization, choice of profession, etc.) and well-being in the workplace among representatives of various positions. Sample. 11 558 employees (7,373 women, 63.8%, and 4,175 men, 36.2%) of a large energy company in the Russian Federation. Methods. Th e participants fi lled in the questionnaire on the components of job satisfaction, the scales of the work involvement, the boredom at the workplace, the emotional exhaustion and initiative. Results. Th e representatives of diff erent positions are most satisfi ed with the benefi ts of their work for people, with their choice of profession and the profession itself and the labour process but they are less satisfi ed with the working conditions, payment, management and professional prospect. As for representatives of all the positions, involvement in the work, a low level of boredom and emotional exhaustion and initiative depend on not only the satisfaction with working conditions, payment, management and the professional prospect but also on the satisfaction with the choice of profession, the profession itself, its content, the benefi t to people and the labour process. Conclusion. Regardless of the position and satisfaction with working conditions, the satisfaction with the choice of profession, its content and benefi ts for people and the labour itself predict the involvement in work, initiative, a low level of emotional exhaustion and the boredom at work.
Toward Physically Plausible Data-Driven Models: A Novel Neural Network Approach to Symbolic Regression

Many real-world systems can be described by mathematical models that are human-comprehensible, easy to analyze and help explain the system's behavior. Symbolic regression is a method that can automatically generate such models from data. Historically, symbolic regression has been predominantly realized by genetic programming, a method that evolves populations of candidate solutions that are subsequently modified by genetic operators crossover and mutation. However, this approach suffers from several deficiencies: it does not scale well with the number of variables and samples in the training data - models tend to grow in size and complexity without an adequate accuracy gain, and it is hard to fine-tune the model coefficients using just genetic operators. Recently, neural networks have been applied to learn the whole analytic model, i.e., its structure and the coefficients, using gradient-based optimization algorithms. This paper proposes a novel neural network-based symbolic regression method that constructs physically plausible models based on even very small training data sets and prior knowledge about the system. The method employs an adaptive weighting scheme to effectively deal with multiple loss function terms and an epoch-wise learning process to reduce the chance of getting stuck in poor local optima. Furthermore, we propose a parameter-free method for choosing the model with the best interpolation and extrapolation performance out of all the models generated throughout the whole learning process. We experimentally evaluate the approach on four test systems: the TurtleBot 2 mobile robot, the magnetic manipulation system, the equivalent resistance of two resistors in parallel, and the longitudinal force of the anti-lock braking system. The results clearly show the potential of the method to find parsimonious models that comply with the prior knowledge provided.
Curriculum-guided Abstractive Summarization for Mental Health Online Posts

Automatically generating short summaries from users' online mental health posts could save counselors' reading time and reduce their fatigue so that they can provide timely responses to those seeking help for improving their mental state. Recent Transformers-based summarization models have presented a promising approach to abstractive summarization. They go beyond sentence selection and extractive strategies to deal with more complicated tasks such as novel word generation and sentence paraphrasing. Nonetheless, these models have a prominent shortcoming; their training strategy is not quite efficient, which restricts the model's performance. In this paper, we include a curriculum learning approach to reweigh the training samples, bringing about an efficient learning procedure. We apply our model on extreme summarization dataset of MentSum posts -- a dataset of mental health related posts from Reddit social media. Compared to the state-of-the-art model, our proposed method makes substantial gains in terms of Rouge and Bertscore evaluation metrics, yielding 3.5% (Rouge-1), 10.4% (Rouge-2), and 4.7% (Rouge-L), 1.5% (Bertscore) relative improvements.
On Suppressing Range of Adaptive Stepsizes of Adam to Improve Generalisation Performance

A number of recent adaptive optimizers improve the generalisation performance of Adam by essentially reducing the variance of adaptive stepsizes to get closer to SGD with momentum. Following the above motivation, we suppress the range of the adaptive stepsizes of Adam by exploiting the layerwise gradient statistics. In particular, at each iteration, we propose to perform three consecutive operations on the second momentum v_t before using it to update a DNN model: (1): down-scaling, (2): epsilon-embedding, and (3): down-translating. The resulting algorithm is referred to as SET-Adam, where SET is a brief notation of the three operations. The down-scaling operation on v_t is performed layerwise by making use of the angles between the layerwise subvectors of v_t and the corresponding all-one subvectors. Extensive experimental results show that SET-Adam outperforms eight adaptive optimizers when training transformers and LSTMs for NLP, and VGG and ResNet for image classification over CIAF10 and CIFAR100 while matching the best performance of the eight adaptive methods when training WGAN-GP models for image generation tasks. Furthermore, SET-Adam produces higher validation accuracies than Adam and AdaBelief for training ResNet18 over ImageNet.
OCR-based Hybrid Image Text Summarizer using Luhn Algorithm with FinetuneTransformer Modelsfor Long Document

The accessibility of an enormous number of image text documents on the internet has expanded the opportunities to develop a system for image text recognition with text summarization. Several approaches used in ATS in the literature are based on extractive and abstractive techniques; however, few implementations of the hybrid approach were observed. This paper employed state-of-the-art transformer models with the Luhn algorithm for extracted texts using Tesseract OCR. Nine models were generated and tested using the hybrid text summarization approach. Using ROUGE metrics, we compared the proposed system finetune abstractive models against existing abstractive models that use the same dataset Xsum. As a result, the finetune model got the highest ROUGE score during evaluation; in ROUGE-1 score was 57%, the ROUGE-2 score was 43%, and the ROUGE-L score was 42%. Furthermore, even when better algorithms and models were available for summarization, the Luhn algorithm and T5 finetune model provided significant results.
Federated Analytics: A survey

Federated analytics (FA) is a privacy-preserving framework for computing data analytics over multiple remote parties (e.g., mobile devices) or silo-ed institutional entities (e.g., hospitals, banks) without sharing the data among parties. Motivated by the practical use cases of federated analytics, we follow a systematic discussion on federated analytics in this article. In particular, we discuss the unique characteristics of federated analytics and how it differs from federated learning. We also explore a wide range of FA queries and discuss various existing solutions and potential use case applications for different FA queries.
Meta-analisis: Pengaruh Media E-learning Terhadap Keterampilan Berpikir Kritis dan Kreatif

E-learning media is the result of technological developments that are currently used to support online and blended learning. Many researches on the development of e-learning media have been carried out, so a re-analysis is needed to find out how much influence the use of e-learning media has on critical and creative thinking skills. This study uses a meta-analysis technique by determining the effect size value. The sample used is 10 articles from 2011-2021. Based on the results of the analysis, it can be seen that the use of e-learning media has a major effect on critical and creative thinking skills. The e-learning media that has an effect on critical thinking skills is role play games (RPG). The e-learning media that has an effect on creative thinking skills is mobile learning. The effect of e-learning media on critical and creative thinking skills is highest at the high school education level. The biggest influence of the use of e-learning media on critical thinking skills is in Java Island, and on creative thinking skills is in the Maluku Islands.