Special Issue "Human and Artificial Intelligence"

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

Deadline for manuscript submissions: 20 August 2022 | Viewed by 4108

Special Issue Editors

Prof. Dr. Alessandro Micarelli
E-Mail Website
Guest Editor
Department of Engineering, Roma Tre University, Roma, Italy
Interests: human–computer interaction; adaptive web-based systems; user modeling; personalized search; recommender systems; artificial intelligence in education
Dr. Giuseppe Sansonetti
E-Mail Website
Guest Editor
Department of Engineering, Roma Tre University, Roma, Italy
Interests: human-computer interaction; user modeling; recommender systems; case-based reasoning; computer vision
Dr. Giuseppe D’Aniello
E-Mail Website
Guest Editor
Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Via Giovanni Paolo II, 132 84084 Fisciano (Sa), Italy
Interests: situation awareness; computational intelligence; granular computing; knowledge management

Special Issue Information

Dear Colleagues,

In recent years, significant advances have been made toward the realization of General Artificial Intelligence, especially in the Machine Learning (ML) (e.g., Deep Learning) domain. Several real-world tasks, however, cannot yet be solved by machines alone. Systems are hence needed that rely on the integration of Human and Artificial Intelligence to solve the most complex problems. In many academic and industrial intelligent agents, communication between humans and computers is a key factor. Many challenges, however, can hinder successful cooperation between the two actors. ML algorithms, for instance, fail to provide explanations for their actions, while human cognitive overload and human out-of-the-loop syndrome may result in lower performance.

The goal of this Special Issue is to supply a varied and thorough collection of high-quality contributions that present emerging approaches and applications focused on human–machine collaboration and cooperation.

Our intent is to foster successful research, highlighting new methods and frameworks that may inspire researchers to achieve even better findings.

Topics of interest include but are not limited to the following:

  • New technologies and frameworks that support human–machine interaction and human–machine collaborative intelligence;
  • Machine learning (e.g., deep learning) to understand human behavior;
  • Explainable Artificial Intelligence;
  • Human factors in Artificial Intelligence;
  • Human teaming with autonomous systems;
  • Situation-aware intelligent systems;
  • Artificial intelligence for cyberphysical–social systems;
  • Emotion Artificial Intelligence (e.g., sentiment analysis);
  • Brain–computer modeling for human–machine cooperation;
  • Decision support systems in different domains (e.g., logistics, smart factory, healthcare);
  • Personalized systems (e.g., user profiling, e-learning, recommender systems).

Prof. Dr. Alessandro Micarelli
Dr. Giuseppe Sansonetti
Dr. Giuseppe D’Aniello
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 2300 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.

Keywords

  • human–computer interaction
  • user modeling
  • recommender systems
  • case-based reasoning
  • computer vision

Published Papers (7 papers)

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Research

Article
Important Features Selection and Classification of Adult and Child from Handwriting Using Machine Learning Methods
Appl. Sci. 2022, 12(10), 5256; https://0-doi-org.brum.beds.ac.uk/10.3390/app12105256 - 23 May 2022
Viewed by 203
Abstract
The classification of different age groups, such as adult and child, based on handwriting is very important due to its various applications in many different fields. In forensics, handwriting classification helps investigators focus on a certain category of writers. This paper aimed to [...] Read more.
The classification of different age groups, such as adult and child, based on handwriting is very important due to its various applications in many different fields. In forensics, handwriting classification helps investigators focus on a certain category of writers. This paper aimed to propose a machine-learning (ML)-based approach for automatically classifying people as adults or children based on their handwritten data. This study utilized two types of handwritten databases: handwritten text and handwritten pattern, which were collected using a pen tablet. The handwritten text database had 57 subjects (adult: 26 vs. child: 31). Each subject (adult or child) wrote the same 30 words using Japanese hiragana characters. The handwritten pattern database had 81 subjects (adult: 42 and child: 39). Each subject (adult or child) drew four different lines as zigzag lines (trace condition and predict condition), and periodic lines (trace condition and predict condition) and repeated these line tasks three times. Handwriting classification of adult and child is performed in three steps: (i) feature extraction; (ii) feature selection; and (iii) classification. We extracted 30 features from both handwritten text and handwritten pattern datasets. The most efficient features were selected using sequential forward floating selection (SFFS) method and the optimal parameters were selected. Then two ML-based approaches, namely, support vector machine (SVM) and random forest (RF) were applied to classify adult and child. Our findings showed that RF produced up to 93.5% accuracy for handwritten text and 89.8% accuracy for handwritten pattern databases. We hope that this study will provide the evidence of the possibility of classifying adult and child based on handwriting text and handwriting pattern data. Full article
(This article belongs to the Special Issue Human and Artificial Intelligence)
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Article
Provider Fairness for Diversity and Coverage in Multi-Stakeholder Recommender Systems
Appl. Sci. 2022, 12(10), 4984; https://0-doi-org.brum.beds.ac.uk/10.3390/app12104984 - 14 May 2022
Viewed by 363
Abstract
Nowadays, recommender systems (RS) are no longer evaluated only for the accuracy of their recommendations. Instead, there is a requirement for other metrics (e.g., coverage, diversity, serendipity) to be taken into account as well. In this context, the multi-stakeholder RS paradigm (MSRS) has [...] Read more.
Nowadays, recommender systems (RS) are no longer evaluated only for the accuracy of their recommendations. Instead, there is a requirement for other metrics (e.g., coverage, diversity, serendipity) to be taken into account as well. In this context, the multi-stakeholder RS paradigm (MSRS) has gained significant popularity, as it takes into consideration all beneficiaries involved, from item providers to simple users. In this paper, the goal is to provide fair recommendations across item providers in terms of diversity and coverage for users to whom each provider’s items are recommended. This is achieved by following the methodology provided by the literature for solving the recommendation problem as an optimization problem under constraints for coverage and diversity. As the constraints for diversity are quadratic and cannot be solved in sufficient time (NP-Hard problem), we propose a heuristic approach that provides solutions very close to the optimal one, as the proposed approach in the literature for solving diversity constraints was too generic. As a next step, we evaluate the results and identify several weaknesses in the problem formulation as provided in the literature. To this end, we introduce new formulations for diversity and provide a new heuristic approach for the solution of the new optimization problem. Full article
(This article belongs to the Special Issue Human and Artificial Intelligence)
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Article
Outcome Prediction for SARS-CoV-2 Patients Using Machine Learning Modeling of Clinical, Radiological, and Radiomic Features Derived from Chest CT Images
Appl. Sci. 2022, 12(9), 4493; https://0-doi-org.brum.beds.ac.uk/10.3390/app12094493 - 28 Apr 2022
Viewed by 238
Abstract
(1) Background: Chest Computed Tomography (CT) has been proposed as a non-invasive method for confirming the diagnosis of SARS-CoV-2 patients using radiomic features (RFs) and baseline clinical data. The performance of Machine Learning (ML) methods using RFs derived from semi-automatically segmented lungs in [...] Read more.
(1) Background: Chest Computed Tomography (CT) has been proposed as a non-invasive method for confirming the diagnosis of SARS-CoV-2 patients using radiomic features (RFs) and baseline clinical data. The performance of Machine Learning (ML) methods using RFs derived from semi-automatically segmented lungs in chest CT images was investigated regarding the ability to predict the mortality of SARS-CoV-2 patients. (2) Methods: A total of 179 RFs extracted from 436 chest CT images of SARS-CoV-2 patients, and 8 clinical and 6 radiological variables, were used to train and evaluate three ML methods (Least Absolute Shrinkage and Selection Operator [LASSO] regularized regression, Random Forest Classifier [RFC], and the Fully connected Neural Network [FcNN]) for their ability to predict mortality using the Area Under the Curve (AUC) of Receiver Operator characteristic (ROC) Curves. These three groups of variables were used separately and together as input for constructing and comparing the final performance of ML models. (3) Results: All the ML models using only RFs achieved an informative level regarding predictive ability, outperforming radiological assessment, without however reaching the performance obtained with ML based on clinical variables. The LASSO regularized regression and the FcNN performed equally, both being superior to the RFC. (4) Conclusions: Radiomic features based on semi-automatically segmented CT images and ML approaches can aid in identifying patients with a high risk of mortality, allowing a fast, objective, and generalizable method for improving prognostic assessment by providing a second expert opinion that outperforms human evaluation. Full article
(This article belongs to the Special Issue Human and Artificial Intelligence)
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Article
Deep Variational Embedding Representation on Neural Collaborative Filtering Recommender Systems
Appl. Sci. 2022, 12(9), 4168; https://0-doi-org.brum.beds.ac.uk/10.3390/app12094168 - 20 Apr 2022
Viewed by 343
Abstract
Visual representation of user and item relations is an important issue in recommender systems. This is a big data task that helps to understand the underlying structure of the information, and it can be used by company managers and technical staff. Current collaborative [...] Read more.
Visual representation of user and item relations is an important issue in recommender systems. This is a big data task that helps to understand the underlying structure of the information, and it can be used by company managers and technical staff. Current collaborative filtering machine learning models are designed to improve prediction accuracy, not to provide suitable visual representations of data. This paper proposes a deep learning model specifically designed to display the existing relations among users, items, and both users and items. Making use of representative datasets, we show that by setting small embedding sizes of users and items, the recommender system accuracy remains nearly unchanged; it opens the door to the use of bidimensional and three-dimensional representations of users and items. The proposed neural model incorporates variational embedding stages to “unpack” (extend) embedding representations, which facilitates identifying individual samples. It also replaces the join layers in current models with a Lambda Euclidean layer that better catches the space representation of samples. The results show numerical and visual improvements when the proposed model is used compared to the baselines. The proposed model can be used to explain recommendations and to represent demographic features (gender, age, etc.) of samples. Full article
(This article belongs to the Special Issue Human and Artificial Intelligence)
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Article
Using Deep Learning for Collecting Data about Museum Visitor Behavior
Appl. Sci. 2022, 12(2), 533; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020533 - 06 Jan 2022
Cited by 2 | Viewed by 384
Abstract
Nowadays, technology makes it possible to admire objects and artworks exhibited all over the world remotely. We have been able to appreciate this convenience even more in the last period, in which the pandemic has forced us into our homes for a long [...] Read more.
Nowadays, technology makes it possible to admire objects and artworks exhibited all over the world remotely. We have been able to appreciate this convenience even more in the last period, in which the pandemic has forced us into our homes for a long time. However, visiting art sites in person remains a truly unique experience. Even during on-site visits, technology can help make them much more satisfactory, by assisting visitors during the fruition of cultural and artistic resources. To this aim, it is necessary to monitor the active user for acquiring information about their behavior. We, therefore, need systems able to monitor and analyze visitor behavior. The literature proposes several techniques for the timing and tracking of museum visitors. In this article, we propose a novel approach to indoor tracking that can represent a promising and non-expensive solution for some of the critical issues that remain. In particular, the system we propose relies on low-cost equipment (i.e., simple badges and off-the-shelf RGB cameras) and harnesses one of the most recent deep neural networks (i.e., Faster R-CNN) for detecting specific objects in an image or a video sequence with high accuracy. An experimental evaluation performed in a real scenario, namely, the “Exhibition of Fake Art” at Roma Tre University, allowed us to test our system on site. The collected data has proven to be accurate and helpful for gathering insightful information on visitor behavior. Full article
(This article belongs to the Special Issue Human and Artificial Intelligence)
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Article
A Hybrid Recommender System for HCI Design Pattern Recommendations
Appl. Sci. 2021, 11(22), 10776; https://0-doi-org.brum.beds.ac.uk/10.3390/app112210776 - 15 Nov 2021
Viewed by 495
Abstract
User interface design patterns are acknowledged as a standard solution to recurring design problems. The heterogeneity of existing design patterns makes the selection of relevant ones difficult. To tackle these concerns, the current work contributes in a twofold manner. The first contribution is [...] Read more.
User interface design patterns are acknowledged as a standard solution to recurring design problems. The heterogeneity of existing design patterns makes the selection of relevant ones difficult. To tackle these concerns, the current work contributes in a twofold manner. The first contribution is the development of a recommender system for selecting the most relevant design patterns in the Human Computer Interaction (HCI) domain. This system introduces a hybrid approach that combines text-based and ontology-based techniques and is aimed at using semantic similarity along with ontology models to retrieve appropriate HCI design patterns. The second contribution addresses the validation of the proposed recommender system regarding the acceptance intention towards our system by assessing the perceived experience and the perceived accuracy. To this purpose, we conducted a user-centric evaluation experiment wherein participants were invited to fill pre-study and post-test questionnaires. The findings of the evaluation study revealed that the perceived experience of the proposed system’s quality and the accuracy of the recommended design patterns were assessed positively. Full article
(This article belongs to the Special Issue Human and Artificial Intelligence)
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Article
A Study of a Gain Based Approach for Query Aspects in Recall Oriented Tasks
Appl. Sci. 2021, 11(19), 9075; https://0-doi-org.brum.beds.ac.uk/10.3390/app11199075 - 29 Sep 2021
Cited by 2 | Viewed by 408
Abstract
Evidence-based healthcare integrates the best research evidence with clinical expertise in order to make decisions based on the best practices available. In this context, the task of collecting all the relevant information, a recall oriented task, in order to take the right decision [...] Read more.
Evidence-based healthcare integrates the best research evidence with clinical expertise in order to make decisions based on the best practices available. In this context, the task of collecting all the relevant information, a recall oriented task, in order to take the right decision within a reasonable time frame has become an important issue. In this paper, we investigate the problem of building effective Consumer Health Search (CHS) systems that use query variations to achieve high recall and fulfill the information needs of health consumers. In particular, we study an intent-aware gain metric used to estimate the amount of missing information and make a prediction about the achievable recall for each query reformulation during a search session. We evaluate and propose alternative formulations of this metric using standard test collections of the CLEF 2018 eHealth Evaluation Lab CHS. Full article
(This article belongs to the Special Issue Human and Artificial Intelligence)
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