Self-Supervised Representation Learning for Online Handwriting Text Classification

Self-supervised learning offers an efficient way of extracting rich representations from various types of unlabeled data while avoiding the cost of annotating large-scale datasets. This is achievable by designing a pretext task to form pseudo labels with respect to the modality and domain of the data. Given the evolving applications of online handwritten texts, in this study, we propose the novel Part of Stroke Masking (POSM) as a pretext task for pretraining models to extract informative representations from the online handwriting of individuals in English and Chinese languages, along with two suggested pipelines for fine-tuning the pretrained models. To evaluate the quality of the extracted representations, we use both intrinsic and extrinsic evaluation methods. The pretrained models are fine-tuned to achieve state-of-the-art results in tasks such as writer identification, gender classification, and handedness classification, also highlighting the superiority of utilizing the pretrained models over the models trained from scratch

Women in Artificial intelligence (AI)

This Special Issue, entitled "Women in Artificial Intelligence" includes 17 papers from leading women scientists. The papers cover a broad scope of research areas within Artificial Intelligence, including machine learning, perception, reasoning or planning, among others. The papers have applications to relevant fields, such as human health, finance, or education. It is worth noting that the Issue includes three papers that deal with different aspects of gender bias in Artificial Intelligence. All the papers have a woman as the first author. We can proudly say that these women are from countries worldwide, such as France, Czech Republic, United Kingdom, Australia, Bangladesh, Yemen, Romania, India, Cuba, Bangladesh and Spain. In conclusion, apart from its intrinsic scientific value as a Special Issue, combining interesting research works, this Special Issue intends to increase the invisibility of women in AI, showing where they are, what they do, and how they contribute to developments in Artificial Intelligence from their different places, positions, research branches and application fields. We planned to issue this book on the on Ada Lovelace Day (11/10/2022), a date internationally dedicated to the first computer programmer, a woman who had to fight the gender difficulties of her times, in the XIX century. We also thank the publisher for making this possible, thus allowing for this book to become a part of the international activities dedicated to celebrating the value of women in ICT all over the world. With this book, we want to pay homage to all the women that contributed over the years to the field of AI

Strategies for the intelligent selection of components

It is becoming common to build applications as component-intensive systems - a mixture of fresh code and existing components. For application developers the selection of components to incorporate is key to overall system quality - so they want the `best\u27. For each selection task, the application developer will de ne requirements for the ideal component and use them to select the most suitable one. While many software selection processes exist there is a lack of repeatable, usable, exible, automated processes with tool support. This investigation has focussed on nding and implementing strategies to enhance the selection of software components. The study was built around four research elements, targeting characterisation, process, strategies and evaluation. A Post-positivist methodology was used with the Spiral Development Model structuring the investigation. Data for the study is generated using a range of qualitative and quantitative methods including a survey approach, a range of case studies and quasiexperiments to focus on the speci c tuning of tools and techniques. Evaluation and review are integral to the SDM: a Goal-Question-Metric (GQM)-based approach was applied to every Spiral

Conflicts, integration, hybridization of subcultures: An ecological approach to the case of queercore

This paper investigates the case study of queercore, providing a socio-historical analysis of its subcultural production, in the terms of what Michel Foucault has called archaeology of knowledge (1969). In particular, we will focus on: the self-definition of the movement; the conflicts between the two merged worlds of punk and queer culture; the \u201cinternal-subcultural\u201d conflicts between both queercore and punk, and between queercore and gay\lesbian music culture; the political aspects of differentiation. In the conclusion, we will offer an innovative theoretical proposal about the interpretation of subcultures in ecological and semiotic terms, combining the contribution of the American sociologist Andrew Abbot and of the Russian semiologist Jurij Michajlovi\u10d Lotma

Job Satisfaction, Organizational Commitment, and Turnover Intention of Online Teachers in the K-12 Setting

The purpose of this study was to measure and explore factors influencing K-12 online teacher job satisfaction, organizational commitment, and turnover intentions K-12 online education. Using Maslow’s Hierarchy of Needs (1954), Herzberg’s Two-Factor Theory of Satisfaction (1959, 1968), Meyer and Allen’s measure of Organizational Commitment (1997), and Fishbein and Ajzen’s Theory of Reasoned Action and Planned Behavior (1975), this mixed-methods study was conducted in public, private, charter, for-profit, and not-for-profit K-12 online schools in a single Southeastern state. The researcher used a sequential explanatory design by collecting and analyzing quantitative data and then qualitative data in two consecutive phases. Using a quantitative survey design, the study included responses from 105 participants. The results revealed that K-12 online teachers have a moderate-high level of job satisfaction, which correlates to their affective commitment to their organization and their intent to remain teaching in the online setting in the immediate, intermediate, and long-term future. Participants identified flexibility, meeting student needs, technical support and their professional community as the most satisfying aspects of their job, while compensation, workload, missing face-to-face interaction with students, and inactive students were identified as least satisfying. A logistic regression model indicated schedule flexibility, mentoring, number of students, number of years teaching experience, and affective commitment are predictors of online teacher’s likelihood of turnover. In the second phase of the study, eight qualitative focus group interviews were conducted and analyzed using a constant comparative method; these results confirmed and expounded upon the quantitative findings in phase one. These results inform K-12 online school leaders who seek to retain new hires of statistically significant variables that influence teacher retention