Women in Artificial intelligence (AI)

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (15 December 2021) | Viewed by 49152

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Head of Researhc Group ITAKA, Research Group ITAKA, Department of Computer Science and Mathematics, Universitat Rovira i Virgili Tarragona, 43007 Catalonia, Spain
Interests: decision support systems, aggregation operators, management of linguistic and semantic data, models of uncertainty, machine learning and deep learning

E-Mail Website
Guest Editor
Research Group on Knowledge Engineering and Machine Learning at Intelligent Data Science and Artificial Intelligence Research Center, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
Interests: big data; statistics; artificial intelligence; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Gender equality is one of the objectives of the ONU Agenda 2030. In the areas denominated STEM (Science, Technology, Engineering, and Mathematics), the bias of gender is particularly exaggerated. The number of male researchers working in STEM is much larger than for women, and this is a constant in countries all over the world, from Europe to USA. This imbalance affects the visibility of the work done by women, which is difficult to find among a large number of male names. In the field of Artificial Intelligence, this male bias influences the way intelligent systems are developed, designed, and conceived. This may have a large impact on the future world, where digital transformation seems to be connected to the new Artificial Intelligence developments.

This Special Issue aims to gather research work done by women in a single journal issue in order to enhance the visibility. This may also contribute to greater dissemination of the exceptional research being done by female scientists.

Because of the relevance of Artificial Intelligence nowadays, this Special Issue is focused on research conducted in the field of Artificial Intelligence, which is a STEM discipline that has increased in interest and applicability in recent years. Thus, this Special Issue will provide an attractive compendium of the AI research led by women over a wide perspective, with focus on real social or industrial applications to health, mobility, or any other topic relevant for digital society development, and new theoretical contributions to all branches of the AI field, from reasoning to image processing, welcoming voice, natural language processing, and social AI, among others.

The two Guest Editors are women involved in gender equality initiatives in Catalonia (Spain). Dr. Karina Gibert is President of the working group donesIAcat (women in AI in Catalonia) in the Catalan Association for Artificial Intelligence (a chapter of the EurAI association). Dr. Aida Valls is Chair of donesIAcat in the region of Tarragona (south Catalonia). Both have a long trajectory of research in AI and working to bridge the gender gap in Artificial Intelligence as well as Informatics Engineering more generally. Hence, we are pleased to serve as Guest Editors of this Special Issue focused on the AI field. The goal is to highlight the women working on cutting edge topics in AI, both from a theoretical and applied point of view.

We encourage women scientists leading research on AI to submit an original manuscript to this Special Issue. Submissions must have a woman as first author to be considered for inclusion.

Dr. Aida Valls
Prof. Dr. Karina Gibert
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (18 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Editorial

Jump to: Research

7 pages, 2205 KiB  
Editorial
Women in Artificial Intelligence
by Aida Valls and Karina Gibert
Appl. Sci. 2022, 12(19), 9639; https://doi.org/10.3390/app12199639 - 26 Sep 2022
Viewed by 2529
Abstract
Artificial Intelligence (AI) research has expanded very quickly in recent years due to the increase in data and resources, along with the engagement of companies in proposing many challenging applications [...] Full article
(This article belongs to the Special Issue Women in Artificial intelligence (AI))
Show Figures

Figure 1

Research

Jump to: Editorial

21 pages, 1801 KiB  
Article
Intersectional Study of the Gender Gap in STEM through the Identification of Missing Datasets about Women: A Multisided Problem
by Genoveva Vargas-Solar
Appl. Sci. 2022, 12(12), 5813; https://doi.org/10.3390/app12125813 - 08 Jun 2022
Cited by 6 | Viewed by 3078
Abstract
This paper discusses the problem of missing datasets for analysing and exhibiting the role of women in STEM with a particular focus on computer science (CS), artificial intelligence (AI) and data science (DS). It discusses the problem in a concrete case of a [...] Read more.
This paper discusses the problem of missing datasets for analysing and exhibiting the role of women in STEM with a particular focus on computer science (CS), artificial intelligence (AI) and data science (DS). It discusses the problem in a concrete case of a global south country (i.e., Mexico). Our study aims to point out missing datasets to identify invisible information regarding women and the implications when studying the gender gap in different STEM disciplines. Missing datasets about women in STEM show that the first step to understanding gender imbalance in STEM is building women’s history by “completing” existing datasets. Full article
(This article belongs to the Special Issue Women in Artificial intelligence (AI))
Show Figures

Figure 1

15 pages, 321 KiB  
Article
Ethical Issues in AI-Enabled Disease Surveillance: Perspectives from Global Health
by Ann Borda, Andreea Molnar, Cristina Neesham and Patty Kostkova
Appl. Sci. 2022, 12(8), 3890; https://doi.org/10.3390/app12083890 - 12 Apr 2022
Cited by 5 | Viewed by 4473
Abstract
Infectious diseases, as COVID-19 is proving, pose a global health threat in an interconnected world. In the last 20 years, resistant infectious diseases such as severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), H1N1 influenza (swine flu), Ebola virus, Zika virus, [...] Read more.
Infectious diseases, as COVID-19 is proving, pose a global health threat in an interconnected world. In the last 20 years, resistant infectious diseases such as severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), H1N1 influenza (swine flu), Ebola virus, Zika virus, and now COVID-19 have been impacting global health defences, and aggressively flourishing with the rise of global travel, urbanization, climate change, and ecological degradation. In parallel, this extraordinary episode in global human health highlights the potential for artificial intelligence (AI)-enabled disease surveillance to collect and analyse vast amounts of unstructured and real-time data to inform epidemiological and public health emergency responses. The uses of AI in these dynamic environments are increasingly complex, challenging the potential for human autonomous decisions. In this context, our study of qualitative perspectives will consider a responsible AI framework to explore its potential application to disease surveillance in a global health context. Thus far, there is a gap in the literature in considering these multiple and interconnected levels of disease surveillance and emergency health management through the lens of a responsible AI framework. Full article
(This article belongs to the Special Issue Women in Artificial intelligence (AI))
18 pages, 1679 KiB  
Article
A Comparative Study of Two Rule-Based Explanation Methods for Diabetic Retinopathy Risk Assessment
by Najlaa Maaroof, Antonio Moreno, Aida Valls, Mohammed Jabreel and Marcin Szeląg
Appl. Sci. 2022, 12(7), 3358; https://doi.org/10.3390/app12073358 - 25 Mar 2022
Cited by 2 | Viewed by 1716
Abstract
Understanding the reasons behind the decisions of complex intelligent systems is crucial in many domains, especially in healthcare. Local explanation models analyse a decision on a single instance, by using the responses of the system to the points in its neighbourhood to build [...] Read more.
Understanding the reasons behind the decisions of complex intelligent systems is crucial in many domains, especially in healthcare. Local explanation models analyse a decision on a single instance, by using the responses of the system to the points in its neighbourhood to build a surrogate model. This work makes a comparative analysis of the local explanations provided by two rule-based explanation methods on RETIPROGRAM, a system based on a fuzzy random forest that analyses the health record of a diabetic person to assess his/her degree of risk of developing diabetic retinopathy. The analysed explanation methods are C-LORE-F (a variant of LORE that builds a decision tree) and DRSA (a method based on rough sets that builds a set of rules). The explored methods gave good results in several metrics, although there is room for improvement in the generation of counterfactual examples. Full article
(This article belongs to the Special Issue Women in Artificial intelligence (AI))
Show Figures

Figure 1

18 pages, 26659 KiB  
Article
Building a Territorial Working Group to Reduce Gender Gap in the Field of Artificial Intelligence
by Karina Gibert and Aida Valls
Appl. Sci. 2022, 12(6), 3129; https://doi.org/10.3390/app12063129 - 18 Mar 2022
Cited by 3 | Viewed by 2050
Abstract
The gender gap (both at vocational and professional sides) in Artificial Intelligence (AI), and scientific and technological fields in general, is one of the most critical challenges that the current digital society must solve. This paper describes the proposal of the gender commission [...] Read more.
The gender gap (both at vocational and professional sides) in Artificial Intelligence (AI), and scientific and technological fields in general, is one of the most critical challenges that the current digital society must solve. This paper describes the proposal of the gender commission donesIAcat to create a gender working group formed by Catalan AI scientists and professionals who work in a network for bridging this gap. The main objectives for letting girls know that they can study and work in the AI field are presented in this paper. A general methodological framework is proposed, following the internal organization of the Catalan group donesIAcat. Several key actions are explained and classified into six blocks. A relevant contribution of the paper is the definition of the guidelines required to build a territorial network-based structure capable of launching several AI-related activities targeting people at different stages of their life. The activities done at donesIAcat illustrate the possible outcomes of the proposed methodology and show successful initiatives to engage girls in technology and AI. The paper shows the validity of this model for small homogeneous territories where activities can be suitable for the different cities in the region. Proximity is one of the advantages of such a model and one of the reasons for its success. Full article
(This article belongs to the Special Issue Women in Artificial intelligence (AI))
Show Figures

Figure 1

12 pages, 604 KiB  
Article
The Digital Revolution in the Urban Water Cycle and Its Ethical–Political Implications: A Critical Perspective
by Lucia Alexandra Popartan, Àtia Cortés, Manel Garrido-Baserba, Marta Verdaguer, Manel Poch and Karina Gibert
Appl. Sci. 2022, 12(5), 2511; https://doi.org/10.3390/app12052511 - 28 Feb 2022
Cited by 6 | Viewed by 2335
Abstract
The development and application of new forms of automation and monitoring, data mining, and the use of AI data sources and knowledge management tools in the water sector has been compared to a ‘digital revolution’. The state-of-the-art literature has analysed this transformation from [...] Read more.
The development and application of new forms of automation and monitoring, data mining, and the use of AI data sources and knowledge management tools in the water sector has been compared to a ‘digital revolution’. The state-of-the-art literature has analysed this transformation from predominantly technical and positive perspectives, emphasising the benefits of digitalisation in the water sector. Meanwhile, there is a conspicuous lack of critical literature on this topic. To bridge this gap, the paper advances a critical overview of the state-of-the art scholarship on water digitalisation, looking at the sociopolitical and ethical concerns these technologies generate. We did this by analysing relevant AI applications at each of the three levels of the UWC: technical, operational, and sociopolitical. By drawing on the precepts of urban political ecology, we propose a hydrosocial approach to the so-called ‘digital water ‘, which aims to overcome the one-sidedness of the technocratic and/or positive approaches to this issue. Thus, the contribution of this article is a new theoretical framework which can be operationalised in order to analyse the ethical–political implications of the deployment of AI in urban water management. From the overview of opportunities and concerns presented in this paper, it emerges that a hydrosocial approach to digital water management is timely and necessary. The proposed framework envisions AI as a force in the service of the human right to water, the implementation of which needs to be (1) critical, in that it takes into consideration gender, race, class, and other sources of discrimination and orients algorithms according to key principles and values; (2) democratic and participatory, i.e., it combines a concern for efficiency with sensitivity to issues of fairness or justice; and (3) interdisciplinary, meaning that it integrates social sciences and natural sciences from the outset in all applications. Full article
(This article belongs to the Special Issue Women in Artificial intelligence (AI))
Show Figures

Figure 1

21 pages, 3517 KiB  
Article
Probabilistic Models for Competence Assessment in Education
by Alejandra López de Aberasturi Gómez, Jordi Sabater-Mir and Carles Sierra
Appl. Sci. 2022, 12(5), 2368; https://doi.org/10.3390/app12052368 - 24 Feb 2022
Cited by 1 | Viewed by 1304
Abstract
Probabilistic models of competence assessment join the benefits of automation with human judgment. We start this paper by replicating two preexisting probabilistic models of peer assessment (PG1-bias and PAAS). Despite the use that both make of probability theory, the [...] Read more.
Probabilistic models of competence assessment join the benefits of automation with human judgment. We start this paper by replicating two preexisting probabilistic models of peer assessment (PG1-bias and PAAS). Despite the use that both make of probability theory, the approach of these models is radically different. While PG1-bias is purely Bayesian, PAAS models the evaluation process in a classroom as a multiagent system, where each actor relies on the judgment of others as long as their opinions coincide. To reconcile the benefits of Bayesian inference with the concept of trust posed in PAAS, we propose a third peer evaluation model that considers the correlations between any pair of peers who have evaluated someone in common: PG-bivariate. The rest of the paper is devoted to a comparison with synthetic data from these three models. We show that PG1-bias produces predictions with lower root mean squared error (RMSE) than PG-bivariate. However, both models display similar behaviors when assessing how to choose the next assignment to be graded by a peer, with an “RMSE decreasing policy” reporting better results than a random policy. Fair comparisons among the three models show that PG1-bias makes the lowest error in situations of scarce ground truths. Nevertheless, once nearly 20% of the teacher’s assessments are introduced, PAAS sometimes exceeds the quality of PG1-bias’ predictions by following an entropy minimization heuristic. PG-bivariate, our new proposal to reconcile PAAS’ trust-based approach with PG1-bias’ theoretical background, obtains a similar percentage of error values to those of the original models. Future work includes applying the models to real experimental data and exploring new heuristics to determine which teacher’s grade should be obtained next to minimize the overall error. Full article
(This article belongs to the Special Issue Women in Artificial intelligence (AI))
Show Figures

Figure 1

14 pages, 1093 KiB  
Article
Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals
by Aura Hernández-Sabaté, José Yauri, Pau Folch, Miquel Àngel Piera and Debora Gil
Appl. Sci. 2022, 12(5), 2298; https://doi.org/10.3390/app12052298 - 22 Feb 2022
Cited by 17 | Viewed by 3240
Abstract
The commercial flightdeck is a naturally multi-tasking work environment, one in which interruptions are frequent come in various forms, contributing in many cases to aviation incident reports. Automatic characterization of pilots’ workloads is essential to preventing these kind of incidents. In addition, minimizing [...] Read more.
The commercial flightdeck is a naturally multi-tasking work environment, one in which interruptions are frequent come in various forms, contributing in many cases to aviation incident reports. Automatic characterization of pilots’ workloads is essential to preventing these kind of incidents. In addition, minimizing the physiological sensor network as much as possible remains both a challenge and a requirement. Electroencephalogram (EEG) signals have shown high correlations with specific cognitive and mental states, such as workload. However, there is not enough evidence in the literature to validate how well models generalize in cases of new subjects performing tasks with workloads similar to the ones included during the model’s training. In this paper, we propose a convolutional neural network to classify EEG features across different mental workloads in a continuous performance task test that partly measures working memory and working memory capacity. Our model is valid at the general population level and it is able to transfer task learning to pilot mental workload recognition in a simulated operational environment. Full article
(This article belongs to the Special Issue Women in Artificial intelligence (AI))
Show Figures

Figure 1

29 pages, 2629 KiB  
Article
Bootstrap–CURE: A Novel Clustering Approach for Sensor Data—An Application to 3D Printing Industry
by Shikha Suman, Ashutosh Karna and Karina Gibert
Appl. Sci. 2022, 12(4), 2191; https://doi.org/10.3390/app12042191 - 19 Feb 2022
Cited by 2 | Viewed by 1756
Abstract
The agenda of Industry 4.0 highlights smart manufacturing by making machines smart enough to make data-driven decisions. Large-scale 3D printers, being one of the important pillars in Industry 4.0, are equipped with smart sensors to continuously monitor print processes and make automated decisions. [...] Read more.
The agenda of Industry 4.0 highlights smart manufacturing by making machines smart enough to make data-driven decisions. Large-scale 3D printers, being one of the important pillars in Industry 4.0, are equipped with smart sensors to continuously monitor print processes and make automated decisions. One of the biggest challenges in decision autonomy is to consume data quickly along the process and extract knowledge from the printer, suitable for improving the printing process. This paper presents the innovative unsupervised learning approach, bootstrap–CURE, to decode the sensor patterns and operation modes of 3D printers by analyzing multivariate sensor data. An automatic technique to detect the suitable number of clusters using the dendrogram is developed. The proposed methodology is scalable and significantly reduces computational cost as compared to classical CURE. A distinct combination of the 3D printer’s sensors is found, and its impact on the printing process is also discussed. A real application is presented to illustrate the performance and usefulness of the proposal. In addition, a new state of the art for sensor data analysis is presented. Full article
(This article belongs to the Special Issue Women in Artificial intelligence (AI))
Show Figures

Figure 1

15 pages, 291 KiB  
Article
Artificial Intelligence and Women Researchers in the Czech Republic
by Lenka Lhotska and Olga Stepankova
Appl. Sci. 2022, 12(3), 1465; https://doi.org/10.3390/app12031465 - 29 Jan 2022
Cited by 1 | Viewed by 1689
Abstract
Artificial intelligence as a research area has been continuously growing for several decades. Many applications were developed in various domains. Medicine and health care have attracted more intensive attention thanks to rapid technological development that has accelerated generation of large volumes of data [...] Read more.
Artificial intelligence as a research area has been continuously growing for several decades. Many applications were developed in various domains. Medicine and health care have attracted more intensive attention thanks to rapid technological development that has accelerated generation of large volumes of data requiring intelligent analysis and evaluation. This article illustrates, through examples of women researchers and selected AI projects in medicine, the wide spectrum of applications developed during the last fifteen years in the Czech Republic, and in particular at the Czech Technical University in Prague. Women researchers played an important and irreplaceable role since the advent of AI research in the Czech Republic. By their example, they motivated many young female students to join the community and start their research career in the AI area. They frequently participated in research projects led by the senior women researchers. The presented overview of projects illustrates the diversity of the medical area and the potential of AI methods that can be used for solving data- and knowledge-intensive problems. We briefly touch on the AI study programs. In conclusion, we point out the future challenges in AI and its applications in medicine and health care. Full article
(This article belongs to the Special Issue Women in Artificial intelligence (AI))
21 pages, 39174 KiB  
Article
Comparison Uncertainty of Different Types of Membership Functions in T2FLS: Case of International Financial Market
by Zuzana Janková and Eva Rakovská
Appl. Sci. 2022, 12(2), 918; https://doi.org/10.3390/app12020918 - 17 Jan 2022
Cited by 7 | Viewed by 2341
Abstract
This article deals with the determination and comparison of different types of functions of the type-2 interval of fuzzy logic, using a case study on the international financial market. The model is demonstrated on the time series of the leading stock index DJIA [...] Read more.
This article deals with the determination and comparison of different types of functions of the type-2 interval of fuzzy logic, using a case study on the international financial market. The model is demonstrated on the time series of the leading stock index DJIA of the US market. Type-2 Fuzzy Logic membership features are able to include additional uncertainty resulting from unclear, uncertain or inaccurate financial data that are selected as inputs to the model. Data on the financial situation of companies are prone to inaccuracies or incomplete information, which is why the type-2 fuzzy logic application is most suitable for this type of financial analysis. This paper is primarily focused on comparing and evaluating the performance of different types of type-2 fuzzy membership functions with integrated additional uncertainty. For this purpose, several model situations differing in shape and level or degree of uncertainty of membership functions are constructed. The results of this research show that type-2 fuzzy sets with dual membership functions is a suitable expert system for highly chaotic and unstable international stock markets and achieves higher accuracy with the integration of a certain level of uncertainty compared to type-1 fuzzy logic. Full article
(This article belongs to the Special Issue Women in Artificial intelligence (AI))
Show Figures

Figure 1

19 pages, 3690 KiB  
Article
Towards Adaptive Gamification: A Method Using Dynamic Player Profile and a Case Study
by Inmaculada Rodríguez, Anna Puig and Àlex Rodríguez
Appl. Sci. 2022, 12(1), 486; https://doi.org/10.3390/app12010486 - 04 Jan 2022
Cited by 15 | Viewed by 2874
Abstract
The design of gamified experiences following the one-fits-all approach uses the same game elements for all users participating in the experience. The alternative is adaptive gamification, which considers that users have different playing motivations. Some adaptive approaches use a (static) player profile gathered [...] Read more.
The design of gamified experiences following the one-fits-all approach uses the same game elements for all users participating in the experience. The alternative is adaptive gamification, which considers that users have different playing motivations. Some adaptive approaches use a (static) player profile gathered at the beginning of the experience; thus, the user experience fits this player profile uncovered through the use of a player type questionnaire. This paper presents a dynamic adaptive method which takes players’ profiles as initial information and also considers how these profiles change over time based on users’ interactions and opinions. Then, the users are provided with a personalized experience through the use of game elements that correspond to their dynamic playing profile. We describe a case study in the educational context, a course integrated on Nanomoocs, a massive open online course (MOOC) platform. We also present a preliminary evaluation of the approach by means of a simulator with bots that yields promising results when compared to baseline methods. The bots simulate different types of users, not so much to evaluate the effects of gamification (i.e., the completion rate), but to validate the convergence and validity of our method. The results show that our method achieves a low error considering both situations: when the user accurately (Err = 0.0070) and inaccurately (Err = 0.0243) answers the player type questionnaire. Full article
(This article belongs to the Special Issue Women in Artificial intelligence (AI))
Show Figures

Figure 1

14 pages, 379 KiB  
Article
CAIT: A Predictive Tool for Supporting the Book Market Operation Using Social Networks
by Jessie C. Martín Sujo, Elisabet Golobardes i Ribé and Xavier Vilasís Cardona
Appl. Sci. 2022, 12(1), 366; https://doi.org/10.3390/app12010366 - 31 Dec 2021
Cited by 4 | Viewed by 1639
Abstract
A new predictive support tool for the publishing industry is presented in this note. It consists of a combined model of Artificial Intelligence techniques (CAIT) that seeks the most optimal prediction of the number of book copies, finding out which is the best [...] Read more.
A new predictive support tool for the publishing industry is presented in this note. It consists of a combined model of Artificial Intelligence techniques (CAIT) that seeks the most optimal prediction of the number of book copies, finding out which is the best segmentation of the book market, using data from the networks social and the web. Predicted sales appear to be more accurate, applying machine learning techniques such as clustering (in this specific case, KMeans) rather than using current publishing industry expert’s segmentation. This identification has important implications for the publishing sector since the forecast will adjust more to the behavior of the stakeholders than to the skills or knowledge acquired by the experts, which is a certain way that may not be sufficient and/or variable throughout the period. Full article
(This article belongs to the Special Issue Women in Artificial intelligence (AI))
Show Figures

Figure 1

19 pages, 2070 KiB  
Article
An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning
by Amna Asif, Hamid Mukhtar, Fatimah Alqadheeb, Hafiz Farooq Ahmad and Abdulaziz Alhumam
Appl. Sci. 2022, 12(1), 238; https://doi.org/10.3390/app12010238 - 27 Dec 2021
Cited by 15 | Viewed by 3831
Abstract
A mispronunciation of Arabic short vowels can change the meaning of a complete sentence. For this reason, both the students and teachers of Classical Arabic (CA) are required extra practice for correcting students’ pronunciation of Arabic short vowels. That makes the teaching and [...] Read more.
A mispronunciation of Arabic short vowels can change the meaning of a complete sentence. For this reason, both the students and teachers of Classical Arabic (CA) are required extra practice for correcting students’ pronunciation of Arabic short vowels. That makes the teaching and learning task cumbersome for both parties. An intelligent process of students’ evaluation can make learning and teaching easier for both students and teachers. Given that online learning has become a norm these days, modern learning requires assessment by virtual teachers. In our case, the task is about recognizing the exact pronunciation of Arabic alphabets according to the standards. A major challenge in the recognition of precise pronunciation of Arabic alphabets is the correct identification of a large number of short vowels, which cannot be dealt with using traditional statistical audio processing techniques and machine learning models. Therefore, we developed a model that classifies Arabic short vowels using Deep Neural Networks (DNN). The model is constructed from scratch by: (i) collecting a new audio dataset, (ii) developing a neural network architecture, and (iii) optimizing and fine-tuning the developed model through several iterations to achieve high classification accuracy. Given a set of unseen audio samples of uttered short vowels, our proposed model has reached the testing accuracy of 95.77%. We can say that our results can be used by the experts and researchers for building better intelligent learning support systems in Arabic speech processing. Full article
(This article belongs to the Special Issue Women in Artificial intelligence (AI))
Show Figures

Figure 1

19 pages, 1124 KiB  
Article
On Simulating the Propagation and Countermeasures of Hate Speech in Social Networks
by Maite Lopez-Sanchez and Arthur Müller
Appl. Sci. 2021, 11(24), 12003; https://doi.org/10.3390/app112412003 - 16 Dec 2021
Cited by 5 | Viewed by 2659
Abstract
Hate speech expresses prejudice and discrimination based on actual or perceived innate characteristics such as gender, race, religion, ethnicity, colour, national origin, disability or sexual orientation. Research has proven that the amount of hateful messages increases inevitably on online social media. Although hate [...] Read more.
Hate speech expresses prejudice and discrimination based on actual or perceived innate characteristics such as gender, race, religion, ethnicity, colour, national origin, disability or sexual orientation. Research has proven that the amount of hateful messages increases inevitably on online social media. Although hate propagators constitute a tiny minority—with less than 1% participants—they create an unproportionally high amount of hate motivated content. Thus, if not countered properly, hate speech can propagate through the whole society. In this paper we apply agent-based modelling to reproduce how the hate speech phenomenon spreads within social networks. We reuse insights from the research literature to construct and validate a baseline model for the propagation of hate speech. From this, three countermeasures are modelled and simulated to investigate their effectiveness in containing the spread of hatred: Education, deferring hateful content, and cyber activism. Our simulations suggest that: (1) Education consititutes a very successful countermeasure, but it is long term and still cannot eliminate hatred completely; (2) Deferring hateful content has a similar—although lower—positive effect than education, and it has the advantage of being a short-term countermeasure; (3) In our simulations, extreme cyber activism against hatred shows the poorest performance as a countermeasure, since it seems to increase the likelihood of resulting in highly polarised societies. Full article
(This article belongs to the Special Issue Women in Artificial intelligence (AI))
Show Figures

Figure 1

15 pages, 501 KiB  
Article
Measuring Polarization in Online Debates
by Teresa Alsinet, Josep Argelich, Ramón Béjar and Santi Martínez
Appl. Sci. 2021, 11(24), 11879; https://doi.org/10.3390/app112411879 - 14 Dec 2021
Cited by 5 | Viewed by 2162
Abstract
Social networks can be a very successful tool to engage users to discuss relevant topics for society. However, there are also some dangers that are associated with them, such as the emergence of polarization in online discussions. Recently, there has been a growing [...] Read more.
Social networks can be a very successful tool to engage users to discuss relevant topics for society. However, there are also some dangers that are associated with them, such as the emergence of polarization in online discussions. Recently, there has been a growing interest to try to understand this phenomenon, as some consider that this can be harmful concerning the building of a healthy society in which citizens get used to polite discussions and even listening to opinions that may be different from theirs. In this work, we face the problem of defining a precise measure that can quantify in a meaningful way the level of polarization present in an online discussion. We focus on the Reddit social network, given that its primary focus is to foster discussions, in contrast to other social networks that have some other uses. Our measure is based on two different characteristics of an online discussion: the existence of a balanced bipartition of the users of the discussion, where one partition contains mainly users in agreement (regarding the topic of the discussion) and the other users in disagreement, and the degree of negativity of the sentiment of the interactions between these two groups of users. We discuss how different characteristics of the discussions affect the value of our polarization measure, and we finally perform an empirical evaluation over different sets of Reddit discussions about diverse classes of topics. Our results seem to indicate that our measure can capture differences in the polarization level of different discussions, which can be further understood when analyzing the values of the different factors used to define the measure. Full article
(This article belongs to the Special Issue Women in Artificial intelligence (AI))
Show Figures

Figure 1

14 pages, 1238 KiB  
Article
Use of Deep Learning to Improve the Computational Complexity of Reconstruction Algorithms in High Energy Physics
by Núria Valls Canudas, Míriam Calvo Gómez, Elisabet Golobardes Ribé and Xavier Vilasis-Cardona
Appl. Sci. 2021, 11(23), 11467; https://doi.org/10.3390/app112311467 - 03 Dec 2021
Cited by 4 | Viewed by 1437
Abstract
The optimization of reconstruction algorithms has become a key aspect in the field of experimental particle physics. Since technology has allowed gradually increasing the complexity of the measurements, the amount of data taken that needs to be interpreted has grown as well. This [...] Read more.
The optimization of reconstruction algorithms has become a key aspect in the field of experimental particle physics. Since technology has allowed gradually increasing the complexity of the measurements, the amount of data taken that needs to be interpreted has grown as well. This is the case with the LHCb experiment at CERN, where a major upgrade currently undergoing will considerably increase the data processing rate. This has presented the need to search for specific reconstruction techniques that aim to accelerate one of the most time consuming reconstruction algorithms in LHCb, the electromagnetic calorimeter clustering. Together with the use of deep learning techniques and the understanding of the current reconstruction algorithm, we propose a method that decomposes the reconstruction process into small parts that can be formulated as a cellular automaton. This approach is shown to benefit the generalized learning of small convolutional neural network architectures and also simplify the training dataset. Final results applied to a complete LHCb simulation reconstruction are compatible in terms of efficiency, and execute in nearly constant time with independence on the complexity of the data. Full article
(This article belongs to the Special Issue Women in Artificial intelligence (AI))
Show Figures

Figure 1

18 pages, 8284 KiB  
Article
AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation
by Syeda Furruka Banu, Md. Mostafa Kamal Sarker, Mohamed Abdel-Nasser, Domenec Puig and Hatem A. Raswan
Appl. Sci. 2021, 11(21), 10132; https://doi.org/10.3390/app112110132 - 28 Oct 2021
Cited by 13 | Viewed by 2576
Abstract
Lung cancer is a deadly cancer that causes millions of deaths every year around the world. Accurate lung nodule detection and segmentation in computed tomography (CT) images is a vital step for diagnosing lung cancer early. Most existing systems face several challenges, such [...] Read more.
Lung cancer is a deadly cancer that causes millions of deaths every year around the world. Accurate lung nodule detection and segmentation in computed tomography (CT) images is a vital step for diagnosing lung cancer early. Most existing systems face several challenges, such as the heterogeneity in CT images and variation in nodule size, shape, and location, which limit their accuracy. In an attempt to handle these challenges, this article proposes a fully automated deep learning framework that consists of lung nodule detection and segmentation models. Our proposed system comprises two cascaded stages: (1) nodule detection based on fine-tuned Faster R-CNN to localize the nodules in CT images, and (2) nodule segmentation based on the U-Net architecture with two effective blocks, namely position attention-aware weight excitation (PAWE) and channel attention-aware weight excitation (CAWE), to enhance the ability to discriminate between nodule and non-nodule feature representations. The experimental results demonstrate that the proposed system yields a Dice score of 89.79% and 90.35%, and an intersection over union (IoU) of 82.34% and 83.21% on the publicly available LUNA16 and LIDC-IDRI datasets, respectively. Full article
(This article belongs to the Special Issue Women in Artificial intelligence (AI))
Show Figures

Figure 1

Back to TopTop