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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: closed (20 August 2022) | Viewed by 62520

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

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, Italy
Interests: situation awareness; computational intelligence; granular computing; knowledge management
Special Issues, Collections and Topics in MDPI journals

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 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.

Keywords

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

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Published Papers (15 papers)

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Editorial

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3 pages, 168 KB  
Editorial
Special Issue on Human and Artificial Intelligence
by Giuseppe Sansonetti, Giuseppe D’Aniello and Alessandro Micarelli
Appl. Sci. 2023, 13(9), 5255; https://doi.org/10.3390/app13095255 - 23 Apr 2023
Cited by 1 | Viewed by 1830
Abstract
Although tremendous advances have been made in recent years, many real-world problems still cannot be solved by machines alone [...] Full article
(This article belongs to the Special Issue Human and Artificial Intelligence)

Research

Jump to: Editorial

19 pages, 2714 KB  
Article
Improving User Experience with Recommender Systems by Informing the Design of Recommendation Messages
by Antoine Falconnet, Constantinos K. Coursaris, Joerg Beringer, Wietske Van Osch, Sylvain Sénécal and Pierre-Majorique Léger
Appl. Sci. 2023, 13(4), 2706; https://doi.org/10.3390/app13042706 - 20 Feb 2023
Cited by 7 | Viewed by 8327
Abstract
Advice-giving systems such as decision support systems and recommender systems (RS) utilize algorithms to provide users with decision support by generating ‘advice’ ranging from tailored alerts for situational exception events to product recommendations based on preferences. Related extant research of user perceptions and [...] Read more.
Advice-giving systems such as decision support systems and recommender systems (RS) utilize algorithms to provide users with decision support by generating ‘advice’ ranging from tailored alerts for situational exception events to product recommendations based on preferences. Related extant research of user perceptions and behaviors has predominantly taken a system-level view, whereas limited attention has been given to the impact of message design on recommendation acceptance and system use intentions. Here, a comprehensive model was developed and tested to explore the presentation choices (i.e., recommendation message characteristics) that influenced users’ confidence in—and likely acceptance of—recommendations generated by the RS. Our findings indicate that the problem and solution-related information specificity of the recommendation increase both user intention and the actual acceptance of recommendations while decreasing the decision-making time; a shorter decision-making time was also observed when the recommendation was structured in a problem-to-solution sequence. Finally, information specificity was correlated with information sufficiency and transparency, confirming prior research with support for the links between user beliefs, user attitudes, and behavioral intentions. Implications for theory and practice are also discussed. Full article
(This article belongs to the Special Issue Human and Artificial Intelligence)
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28 pages, 1375 KB  
Article
Users’ Information Disclosure Behaviors during Interactions with Chatbots: The Effect of Information Disclosure Nudges
by Laurie Carmichael, Sara-Maude Poirier, Constantinos K. Coursaris, Pierre-Majorique Léger and Sylvain Sénécal
Appl. Sci. 2022, 12(24), 12660; https://doi.org/10.3390/app122412660 - 10 Dec 2022
Cited by 15 | Viewed by 5928
Abstract
Drawing from the tension between a company’s desire for customer information to tailor experiences and a consumer’s need for privacy, this study aims to test the effect of two information disclosure nudges on users’ information disclosure behaviors. Whereas previous literature on user-chatbot interaction [...] Read more.
Drawing from the tension between a company’s desire for customer information to tailor experiences and a consumer’s need for privacy, this study aims to test the effect of two information disclosure nudges on users’ information disclosure behaviors. Whereas previous literature on user-chatbot interaction focused on encouraging and increasing users’ disclosures, this study introduces measures that make users conscious of their disclosure behaviors to low and high-sensitivity questions asked by chatbots. A within-subjects laboratory experiment entailed 19 participants interacting with chatbots, responding to pre-tested questions of varying sensitivity while being presented with different information disclosure nudges. The results suggest that question sensitivity negatively impacts users’ information disclosures to chatbots. Moreover, this study suggests that adding a sensitivity signal—presenting the level of sensitivity of the question asked by the chatbot—influences users’ information disclosure behaviors. Finally, the theoretical contributions and managerial implications of the results are discussed. Full article
(This article belongs to the Special Issue Human and Artificial Intelligence)
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17 pages, 12035 KB  
Article
Research on Authentic Signature Identification Method Integrating Dynamic and Static Features
by Jiaxin Lu, Hengnian Qi, Xiaoping Wu, Chu Zhang and Qizhe Tang
Appl. Sci. 2022, 12(19), 9904; https://doi.org/10.3390/app12199904 - 1 Oct 2022
Cited by 8 | Viewed by 4701
Abstract
In many fields of social life, such as justice, finance, communication and so on, signatures are used for identity recognition. The increasingly convenient and extensive application of technology increases the opportunity for forged signatures. How to effectively identify a forged signature is still [...] Read more.
In many fields of social life, such as justice, finance, communication and so on, signatures are used for identity recognition. The increasingly convenient and extensive application of technology increases the opportunity for forged signatures. How to effectively identify a forged signature is still a challenge to be tackled by research. Offline static handwriting has a unique structure and strong interpretability, while online handwriting contains dynamic information, such as timing and pressure. Therefore, this paper proposes an authentic signature identification method, integrating dynamic and static features. The dynamic data and structural style of the signature are extracted by dot matrix pen technology, the global and local features, time and space features are fused and clearer and understandable features are applied to signature identification. At the same time, the classification of a forged signature is more detailed according to the characteristics of signature and a variety of machine learning models and a deep learning network structure are used for classification and recognition. When the number of classifications is 5, it is better to identify simple forgery signatures. When the classification number is 15, the accuracy rate is mostly about 96.7% and the highest accuracy reaches 100% on CNN. This paper focuses on feature extraction, incorporates the advantages of dynamic and static features and improves the classification accuracy of signature identification. Full article
(This article belongs to the Special Issue Human and Artificial Intelligence)
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18 pages, 1517 KB  
Article
Prediction of Eudaimonic and Hedonic Orientation of Movie Watchers
by Elham Motamedi, Francesco Barile and Marko Tkalčič
Appl. Sci. 2022, 12(19), 9500; https://doi.org/10.3390/app12199500 - 22 Sep 2022
Cited by 5 | Viewed by 2141
Abstract
Personality accounts for how individuals differ in their enduring emotional, interpersonal, experiential, attitudinal and motivational styles. Personality, especially in the form of the Five Factor Model, has shown usefulness in personalized systems, such as recommender systems. In this work, we focus on a [...] Read more.
Personality accounts for how individuals differ in their enduring emotional, interpersonal, experiential, attitudinal and motivational styles. Personality, especially in the form of the Five Factor Model, has shown usefulness in personalized systems, such as recommender systems. In this work, we focus on a personality model that is targeted at motivations for multimedia consumption. The model is composed of two dimensions: the (i) eudaimonic orientation of users (EO) and (ii) hedonic orientation of users (HO). While the former accounts for how much a user is interested in content that deals with meaningful topics, the latter accounts for how much a user is interested in the entertaining quality of the content. Our research goal is to devise a model that predicts the EH and HO of users from interaction data with movies, such as ratings. We collected a dataset of 350 users, 703 movies and 3499 ratings. We performed a comparison of various predictive algorithms, as both regression and classification problems. Finally, we demonstrate that our proposed approach is able to predict the EO and HO of users from traces of interactions with movies substantially better than the baseline approaches. The outcomes of this work have implications for exploitation in recommender systems. Full article
(This article belongs to the Special Issue Human and Artificial Intelligence)
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15 pages, 1995 KB  
Article
Adversarial Detection Based on Inner-Class Adjusted Cosine Similarity
by Dejian Guan and Wentao Zhao 
Appl. Sci. 2022, 12(19), 9406; https://doi.org/10.3390/app12199406 - 20 Sep 2022
Cited by 4 | Viewed by 2424
Abstract
Deep neural networks (DNNs) have attracted extensive attention because of their excellent performance in many areas; however, DNNs are vulnerable to adversarial examples. In this paper, we propose a similarity metric called inner-class adjusted cosine similarity (IACS) and apply it to detect adversarial [...] Read more.
Deep neural networks (DNNs) have attracted extensive attention because of their excellent performance in many areas; however, DNNs are vulnerable to adversarial examples. In this paper, we propose a similarity metric called inner-class adjusted cosine similarity (IACS) and apply it to detect adversarial examples. Motivated by the fast gradient sign method (FGSM), we propose to utilize an adjusted cosine similarity which takes both the feature angle and scale information into consideration and therefore is able to effectively discriminate subtle differences. Given the predicted label, the proposed IACS is measured between the features of the test sample and those of the normal samples with the same label. Unlike other detection methods, we can extend our method to extract disentangled features with different deep network models but are not limited to the target model (the adversarial attack model). Furthermore, the proposed method is able to detect adversarial examples crossing attacks, that is, a detector learned with one type of attack can effectively detect other types. Extensive experimental results show that the proposed IACS features can well distinguish adversarial examples and normal examples and achieve state-of-the-art performance. Full article
(This article belongs to the Special Issue Human and Artificial Intelligence)
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18 pages, 3279 KB  
Article
Multi-Granularity Semantic Collaborative Reasoning Network for Visual Dialog
by Hongwei Zhang, Xiaojie Wang, Si Jiang and Xuefeng Li
Appl. Sci. 2022, 12(18), 8947; https://doi.org/10.3390/app12188947 - 6 Sep 2022
Cited by 4 | Viewed by 2385
Abstract
A visual dialog task entails an agent engaging in a multiple round conversation about an image. Notably, one of the main issues is capturing the semantic associations of multiple inputs, such as the questions, dialog history, and image features. Many of the techniques [...] Read more.
A visual dialog task entails an agent engaging in a multiple round conversation about an image. Notably, one of the main issues is capturing the semantic associations of multiple inputs, such as the questions, dialog history, and image features. Many of the techniques use a token or a sentence granularity semantic representation of the question and dialog history to model semantic associations; however, they do not perform collaborative modeling, which limits their efficacy. To overcome this limitation, we propose a multi-granularity semantic collaborative reasoning network to properly support a visual dialog. It employs different granularity semantic representations of the question and dialog history to collaboratively identify the relevant information from multiple inputs based on attention mechanisms. Specifically, the proposed method collaboratively reasons the question-related information from the dialog history based on its granular semantic representations. Then, it collaboratively locates the question-related visual objects in the image by leveraging refined question representations. The experimental results conducted on the VisDial v.1.0 dataset verify the effectiveness of the proposed method, showing the improvements of the best normalized discounted cumulative gain score from 59.37 to 60.98 with a single model, from 60.92 to 62.25 with ensemble models, and from 63.15 to 64.13 with performing multitask learning. Full article
(This article belongs to the Special Issue Human and Artificial Intelligence)
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30 pages, 1219 KB  
Article
Smart Interactive Technologies in the Human-Centric Factory 5.0: A Survey
by Davide Brunetti, Cristina Gena and Fabiana Vernero
Appl. Sci. 2022, 12(16), 7965; https://doi.org/10.3390/app12167965 - 9 Aug 2022
Cited by 43 | Viewed by 5593
Abstract
In this survey paper, we focus on smart interactive technologies and providing a picture of the current state of the art, exploring the way new discoveries and recent technologies changed workers’ operations and activities on the factory floor. We focus in particular on [...] Read more.
In this survey paper, we focus on smart interactive technologies and providing a picture of the current state of the art, exploring the way new discoveries and recent technologies changed workers’ operations and activities on the factory floor. We focus in particular on the Industry 4.0 and 5.0 visions, wherein smart interactive technologies can bring benefits to the intelligent behavior machines can expose in a human-centric AI perspective. We consider smart technologies wherein the intelligence may be in and/or behind the user interfaces, and for both groups we try to highlight the importance of designing them with a human-centric approach, framed in the smart factory context. We review relevant work in the field with the aim of highlighting the pros and cons of each technology and its adoption in the industry. Furthermore, we try to collect guidelines for the human-centric integration of smart interactive technologies in the smart factory. In this wa y, we hope to provide the future designers and adopters of such technologies with concrete help in choosing among different options and implementing them in a user-centric manner. To this aim, surveyed works have been also classified based on the supported task(s) and production process phases/activities: access to knowledge, logistics, maintenance, planning, production, security, workers’ wellbeing, and warehousing. Full article
(This article belongs to the Special Issue Human and Artificial Intelligence)
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14 pages, 883 KB  
Article
Important Features Selection and Classification of Adult and Child from Handwriting Using Machine Learning Methods
by Jungpil Shin, Md. Maniruzzaman, Yuta Uchida, Md. Al Mehedi Hasan, Akiko Megumi, Akiko Suzuki and Akira Yasumura
Appl. Sci. 2022, 12(10), 5256; https://doi.org/10.3390/app12105256 - 23 May 2022
Cited by 20 | Viewed by 4590
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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19 pages, 2484 KB  
Article
Provider Fairness for Diversity and Coverage in Multi-Stakeholder Recommender Systems
by Evangelos Karakolis, Panagiotis Kokkinakos and Dimitrios Askounis
Appl. Sci. 2022, 12(10), 4984; https://doi.org/10.3390/app12104984 - 14 May 2022
Cited by 12 | Viewed by 3500
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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14 pages, 2288 KB  
Article
Outcome Prediction for SARS-CoV-2 Patients Using Machine Learning Modeling of Clinical, Radiological, and Radiomic Features Derived from Chest CT Images
by Lorenzo Spagnoli, Maria Francesca Morrone, Enrico Giampieri, Giulia Paolani, Miriam Santoro, Nico Curti, Francesca Coppola, Federica Ciccarese, Giulio Vara, Nicolò Brandi, Rita Golfieri, Michele Bartoletti, Pierluigi Viale and Lidia Strigari
Appl. Sci. 2022, 12(9), 4493; https://doi.org/10.3390/app12094493 - 28 Apr 2022
Cited by 8 | Viewed by 2334
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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12 pages, 2436 KB  
Article
Deep Variational Embedding Representation on Neural Collaborative Filtering Recommender Systems
by Jesús Bobadilla, Jorge Dueñas, Abraham Gutiérrez and Fernando Ortega
Appl. Sci. 2022, 12(9), 4168; https://doi.org/10.3390/app12094168 - 20 Apr 2022
Cited by 9 | Viewed by 2890
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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21 pages, 7863 KB  
Article
Using Deep Learning for Collecting Data about Museum Visitor Behavior
by Alessio Ferrato, Carla Limongelli, Mauro Mezzini and Giuseppe Sansonetti
Appl. Sci. 2022, 12(2), 533; https://doi.org/10.3390/app12020533 - 6 Jan 2022
Cited by 45 | Viewed by 6527
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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25 pages, 7305 KB  
Article
A Hybrid Recommender System for HCI Design Pattern Recommendations
by Amani Braham, Maha Khemaja, Félix Buendía and Faiez Gargouri
Appl. Sci. 2021, 11(22), 10776; https://doi.org/10.3390/app112210776 - 15 Nov 2021
Cited by 3 | Viewed by 3415
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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15 pages, 486 KB  
Article
A Study of a Gain Based Approach for Query Aspects in Recall Oriented Tasks
by Giorgio Maria Di Nunzio and Guglielmo Faggioli
Appl. Sci. 2021, 11(19), 9075; https://doi.org/10.3390/app11199075 - 29 Sep 2021
Cited by 10 | Viewed by 2418
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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