Using Artificial Intelligence for COVID-19 Detection in Blood Exams: A Comparative Analysis

COVID-19 is an infectious disease that was declared a pandemic by the World Health Organization (WHO) in early March 2020. Since its early development, it has challenged health systems around the world. Although more than 12 billion vaccines have been administered, at the time of writing, it has more than 623 million confirmed cases and more than 6 million deaths reported to the WHO. These numbers continue to grow, soliciting further research efforts to reduce the impacts of such a pandemic. In particular, artificial intelligence techniques have shown great potential in supporting the early diagnosis, detection, and monitoring of COVID-19 infections from disparate data sources. In this work, we aim to make a contribution to this field by analyzing a high-dimensional dataset containing blood sample data from over forty thousand individuals recognized as infected or not with COVID-19. Encompassing a wide range of methods, including traditional machine learning algorithms, dimensionality reduction techniques, and deep learning strategies, our analysis investigates the performance of different classification models, showing that accurate detection of blood infections can be obtained. In particular, an F-score of 84% was achieved by the artificial neural network model we designed for this task, with a rate of 87% correct predictions on the positive class. Furthermore, our study shows that the dimensionality of the original data, i.e. the number of features involved, can be significantly reduced to gain efficiency without compromising the final prediction performance. These results pave the way for further research in this field, confirming that artificial intelligence techniques may play an important role in supporting medical decision-making

DOES FORM-FOCUSED INSTRUCTION AFFECT L2 LEARNERS PERFORMANCE? FOCUS ON GRAMMATICALLY JUDGMENTS

It is one of the goals of research in applied linguistics to gain insight into the process and mechanisms of second language acquisition.  The cornerstone and the single most fundamental change in perspective on the nature of language and language learning is, perhaps, the focus on learners as active creators in their learning process, not as passive recipients.  The present study has two goals.  First, it aims at investigating advanced students’ metalinguistic ability in solving multidimensional grammatical problems.  Second, it is, also, an attempt to highlight the role of focus on form instructions in shaping L2 learners’ performance. The subjects of the present study were forty Egyptian students who were in their fourth year of academic study in the Department of English and Literature, Faculty of Arts, Menufia University, Egypt.  The instrument of this study consisted of (1) pre-test; (2) post-test; and (3) individual interviews.  Two tasks were used: (1) “Sentence Completion” task, and (2) “Error Recognition and Correction” task.  In the first task, a list of 15 incomplete sentences was given to the subjects who were asked to choose the word or phrase to complete the sentence.  The focus, in this task, was on the meaning of the sentence rather than the form, although accurate understanding of the formal properties of language is a must.  In the second task, students were asked to detect the word or phrase that must be changed in order for the sentence to be correct.  A list of 25 sentences was given to the subjects who worked on this task twice.  In the pre-test, no word or phrase was underlined; it is an example of the unfocused correction type.  In the post-test, the same sentences were given to the subjects, with four words underlined, and marked (A), (B), (C) and (D).  It is an example of the focused correction type. Finally, students were interviewed to explain and comment on their performance in the previous tasks.  The data were analyzed both quantitatively and qualitatively. Results were obtained and conclusions were made.It is one of the goals of research in applied linguistics to gain insight into the process and mechanisms of second language acquisition.  A correct understanding of these processes and mechanisms is a prerequisite for an adequate didactic approach.  Relatedly, Morley (1987) points out that during the last twenty years ideas about language learning and language teaching have been changing in some very fundamental ways.  Significant developments in perspectives on the nature of second language learning processes have had a marked effect on language pedagogyThe cornerstone and the single most fundamental change in perspectives on the nature of language and language learning in recent years is, perhaps, the focus on learners as active creators in their learning process, not as passive recipients.  Accordingly, the focus of second language study has shifted from a prominence of contrastive analysis in the 1940s and 1950s and error analysis in the 60s and 70s to interlanguage analysis in the 70s and 80s.  Interlanguage analysis is marked today by “a variety of investigations looking at diverse aspects of learner language” (Morley, 1987: 16).  In this connection, Gass (1983: 273) points out that “it is widely accepted that the language of second language learners, what Selinker (1972 has called ‘interlanguage’ or what (Gass, 1983) has called ‘Learner-language’ is a system in its own right.”  To understand such a system, we should focus on discovering how second language (L2) learners evaluate and correct their own or other people’s utterances, an issue that will be explored in the present study.  In other words, the major point of interest here is L2 learners’ linguistic intuitions and the role of focus on form instruction in making grammaticality judgments

Unlocking the capabilities of explainable fewshot learning in remote sensing

Recent advancements have significantly improved the efficiency and effectiveness of deep learning methods for imagebased remote sensing tasks. However, the requirement for large amounts of labeled data can limit the applicability of deep neural networks to existing remote sensing datasets. To overcome this challenge, fewshot learning has emerged as a valuable approach for enabling learning with limited data. While previous research has evaluated the effectiveness of fewshot learning methods on satellite based datasets, little attention has been paid to exploring the applications of these methods to datasets obtained from UAVs, which are increasingly used in remote sensing studies. In this review, we provide an up to date overview of both existing and newly proposed fewshot classification techniques, along with appropriate datasets that are used for both satellite based and UAV based data. Our systematic approach demonstrates that fewshot learning can effectively adapt to the broader and more diverse perspectives that UAVbased platforms can provide. We also evaluate some SOTA fewshot approaches on a UAV disaster scene classification dataset, yielding promising results. We emphasize the importance of integrating XAI techniques like attention maps and prototype analysis to increase the transparency, accountability, and trustworthiness of fewshot models for remote sensing. Key challenges and future research directions are identified, including tailored fewshot methods for UAVs, extending to unseen tasks like segmentation, and developing optimized XAI techniques suited for fewshot remote sensing problems. This review aims to provide researchers and practitioners with an improved understanding of fewshot learnings capabilities and limitations in remote sensing, while highlighting open problems to guide future progress in efficient, reliable, and interpretable fewshot methods.Comment: Under review, once the paper is accepted, the copyright will be transferred to the corresponding journa

A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.Comment: 30 pages, 15 figure

Human-Computer Interaction

In this book the reader will find a collection of 31 papers presenting different facets of Human Computer Interaction, the result of research projects and experiments as well as new approaches to design user interfaces. The book is organized according to the following main topics in a sequential order: new interaction paradigms, multimodality, usability studies on several interaction mechanisms, human factors, universal design and development methodologies and tools