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Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications

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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 18483

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Dr. Grigoreta-Sofia Cojocar

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Department of Computer Science, Faculty of Mathematics and Computer Science, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania
Interests: formal methods; computational intelligence; software engineering; programming paradigms
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Dr. Adriana-Mihaela Guran

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Department of Computer Science, Faculty of Mathematics and Computer Science, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania
Interests: human computer interaction; graphical user interfaces; software engineering; web technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, many people of different ages interact with multiple devices. Adapting the interaction such that it becomes intuitive and friendly for different categories of people, identifying preferences, or providing empathic interaction are desirable features for any human–computer interface. Integrating the progress from computational intelligence into the human–computer interaction domain provides viable chances to tailored user experiences. Assessing the subjective satisfaction of users is a challenging task, influenced by multiple factors. Identifying users’ emotions during the interaction and establishing relations with the interaction context could provide insights into design flaws and possible improvements. Young children’s interaction with technology would benefit from improvements in the authentication process, emotion identification, and interaction adaptation based on the identified emotions. The current challenges presented by the COVID-19 pandemic show us that more focus should be directed towards smart learning environments.

In this context, this Special Issue focuses on the use of current advances in computational intelligence for supporting the design and assessment of adapted human–computer interaction. This Special Issue provides a platform for researchers from both academia and industry to present their novel and unpublished work in the domains of computational intelligence and human–computer interaction. This will help to foster future research in the emerging field of human–computer interaction and its related fields.

Dr. Grigoreta-Sofia Cojocar
Dr. Adriana-Mihaela Guran
Guest Editors

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Keywords

Computational intelligence
Machine translation
Text processing
Speech processing
Video processing
Visual sense
Face recognition
Fingerprint recognition
Posture recognition
Adaptive systems
Computer vision
Neural networks
Machine learning
Deep learning
Semantic analysis
Natural language processing
Approximated reasoning
User–system interfaces
Affective computing
User experience
Usability
Agent-based systems
Adaptive systems
Requirements engineering for UIs
Intelligent UIs and agents
Computer science techniques for UI development: artificial intelligence, software engineering, etc.
Multicontext usability and user experience evaluation
HCI tools, techniques, and methodologies
Evaluation techniques for HCI
New interaction paradigms for HCI
Training and education applications
Computer-supported learning

Published Papers (9 papers)

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Research

16 pages, 1140 KiB

Open AccessArticle

Influence of Highly Inflected Word Forms and Acoustic Background on the Robustness of Automatic Speech Recognition for Human–Computer Interaction

by Andrej Zgank

Mathematics 2022, 10(5), 711; https://0-doi-org.brum.beds.ac.uk/10.3390/math10050711 - 24 Feb 2022

Cited by 3 | Viewed by 1340

Abstract

Automatic speech recognition is essential for establishing natural communication with a human–computer interface. Speech recognition accuracy strongly depends on the complexity of language. Highly inflected word forms are a type of unit present in some languages. The acoustic background presents an additional important degradation factor influencing speech recognition accuracy. While the acoustic background has been studied extensively, the highly inflected word forms and their combined influence still present a major research challenge. Thus, a novel type of analysis is proposed, where a dedicated speech database comprised solely of highly inflected word forms is constructed and used for tests. Dedicated test sets with various acoustic backgrounds were generated and evaluated with the Slovenian UMB BN speech recognition system. The baseline word accuracy of 93.88% and 98.53% was reduced to as low as 23.58% and 15.14% for the various acoustic backgrounds. The analysis shows that the word accuracy degradation depends on and changes with the acoustic background type and level. The highly inflected word forms’ test sets without background decreased word accuracy from 93.3% to only 63.3% in the worst case. The impact of highly inflected word forms on speech recognition accuracy was reduced with the increased levels of acoustic background and was, in these cases, similar to the non-highly inflected test sets. The results indicate that alternative methods in constructing speech databases, particularly for low-resourced Slovenian language, could be beneficial. Full article

(This article belongs to the Special Issue Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications)

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14 pages, 786 KiB

Open AccessArticle

Improving User’s Experience in Exploring Knowledge Structures: A Gamifying Approach

by Brigitte Breckner, Christian Săcărea and Raul-Robert Zavaczki

Mathematics 2022, 10(5), 709; https://0-doi-org.brum.beds.ac.uk/10.3390/math10050709 - 24 Feb 2022

Viewed by 1274

Abstract

Gamifying user experience while navigating knowledge structures is the new paradigm in online learning and improving human–computer interaction. Learning by playing and learning by doing lie at the core of this approach. Rooted in the paradigm of Conceptual Knowledge Processing and conceptual landscapes of knowledge, this research proposes an immersive experience in 3D knowledge structures, the VR FCA project. We exemplify this experience, at least the amount possible in a journal paper, by using data sets about topological spaces, a mathematical field which is notoriously hard to grasp by beginners or sometimes even at an intermediate level. We present the stages of this project, the technologies we have used in the implementation and also some basics of Formal Concept Analysis (FCA), the mathematical theory which lies at heart of Conceptual Knowledge Processing. Full article

(This article belongs to the Special Issue Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications)

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16 pages, 1539 KiB

Open AccessArticle

The Next Generation of Edutainment Applications for Young Children—A Proposal

by Adriana-Mihaela Guran, Grigoreta-Sofia Cojocar and Laura-Silvia Dioşan

Mathematics 2022, 10(4), 645; https://0-doi-org.brum.beds.ac.uk/10.3390/math10040645 - 19 Feb 2022

Cited by 3 | Viewed by 1687

Abstract

Edutainment applications are a type of software that is designed to be entertaining while also being educational. In the current COVID-19 pandemic context, when children have to stay home due to the social distancing rules, edutainment applications for young children are more and more used each day. However, are these applications ready to take the place of an in-person teacher? In this paper, we propose a new generation of edutainment applications that are more suitable for preschoolers (aged 3–6 years old in our country) and closer to the in-person student–teacher interaction: emotions aware edutainment applications. We discuss the most important challenges that must be overcome in developing this kind of applications (i.e., recognizing children’s emotions, enhancing the edutainment application with emotion awareness, and adapting the interaction flow) and the first steps that we have taken for developing them. Full article

(This article belongs to the Special Issue Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications)

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26 pages, 5415 KiB

Open AccessArticle

Tags’ Recommender to Classify Architectural Knowledge Applying Language Models

by Gilberto Borrego, Samuel González-López and Ramón R. Palacio

Mathematics 2022, 10(3), 446; https://0-doi-org.brum.beds.ac.uk/10.3390/math10030446 - 30 Jan 2022

Cited by 2 | Viewed by 2255

Abstract

Agile global software engineering challenges architectural knowledge (AK) management since face-to-face interactions are preferred over comprehensive documentation, which causes AK loss over time. The AK condensation concept was proposed to reduce AK losing, using the AK shared through unstructured electronic media. A crucial part of this concept is a classification mechanism to ease AK recovery in the future. We developed a Slack complement as a classification mechanism based on social tagging, which recommends tags according to a chat/message topic, using natural language processing (NLP) techniques. We evaluated two tagging modes: NLP-assisted versus alphabetical auto-completion, in terms of correctness and time to select a tag. Fifty-two participants used the complement emulating an agile and global scenario and gave us their complement’s perceptions about usefulness, ease of use, and work integration. Messages tagged through NLP recommendations showed fewer semantic errors, and participants spent less time selecting a tag. They perceived the component as very usable, useful, and easy to be integrated into the daily work. These results indicated that a tag recommendation system is necessary to classify the shared AK accurately and quickly. We will improve the NLP techniques to evaluate AK condensation in a long-term test as future work. Full article

(This article belongs to the Special Issue Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications)

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21 pages, 860 KiB

Open AccessArticle

Application of Fusion of Various Spontaneous Speech Analytics Methods for Improving Far-Field Neural-Based Diarization

by Sergei Astapov, Aleksei Gusev, Marina Volkova, Aleksei Logunov, Valeriia Zaluskaia, Vlada Kapranova, Elena Timofeeva, Elena Evseeva, Vladimir Kabarov and Yuri Matveev

Mathematics 2021, 9(23), 2998; https://0-doi-org.brum.beds.ac.uk/10.3390/math9232998 - 23 Nov 2021

Cited by 1 | Viewed by 1464

Abstract

Recently developed methods in spontaneous speech analytics require the use of speaker separation based on audio data, referred to as diarization. It is applied to widespread use cases, such as meeting transcription based on recordings from distant microphones and the extraction of the target speaker’s voice profiles from noisy audio. However, speech recognition and analysis can be hindered by background and point-source noise, overlapping speech, and reverberation, which all affect diarization quality in conjunction with each other. To compensate for the impact of these factors, there are a variety of supportive speech analytics methods, such as quality assessments in terms of SNR and RT60 reverberation time metrics, overlapping speech detection, instant speaker number estimation, etc. The improvements in speaker verification methods have benefits in the area of speaker separation as well. This paper introduces several approaches aimed towards improving diarization system quality. The presented experimental results demonstrate the possibility of refining initial speaker labels from neural-based VAD data by means of fusion with labels from quality estimation models, overlapping speech detectors, and speaker number estimation models, which contain CNN and LSTM modules. Such fusing approaches allow us to significantly decrease DER values compared to standalone VAD methods. Cases of ideal VAD labeling are utilized to show the positive impact of ResNet-101 neural networks on diarization quality in comparison with basic x-vectors and ECAPA-TDNN architectures trained on 8 kHz data. Moreover, this paper highlights the advantage of spectral clustering over other clustering methods applied to diarization. The overall quality of diarization is improved at all stages of the pipeline, and the combination of various speech analytics methods makes a significant contribution to the improvement of diarization quality. Full article

(This article belongs to the Special Issue Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications)

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22 pages, 25356 KiB

Open AccessArticle

RFaNet: Receptive Field-Aware Network with Finger Attention for Fingerspelling Recognition Using a Depth Sensor

by Shih-Hung Yang, Yao-Mao Cheng, Jyun-We Huang and Yon-Ping Chen

Mathematics 2021, 9(21), 2815; https://0-doi-org.brum.beds.ac.uk/10.3390/math9212815 - 05 Nov 2021

Cited by 3 | Viewed by 1510

Abstract

Automatic fingerspelling recognition tackles the communication barrier between deaf and hearing individuals. However, the accuracy of fingerspelling recognition is reduced by high intra-class variability and low inter-class variability. In the existing methods, regular convolutional kernels, which have limited receptive fields (RFs) and often cannot detect subtle discriminative details, are applied to learn features. In this study, we propose a receptive field-aware network with finger attention (RFaNet) that highlights the finger regions and builds inter-finger relations. To highlight the discriminative details of these fingers, RFaNet reweights the low-level features of the hand depth image with those of the non-forearm image and improves finger localization, even when the wrist is occluded. RFaNet captures neighboring and inter-region dependencies between fingers in high-level features. An atrous convolution procedure enlarges the RFs at multiple scales and a non-local operation computes the interactions between multi-scale feature maps, thereby facilitating the building of inter-finger relations. Thus, the representation of a sign is invariant to viewpoint changes, which are primarily responsible for intra-class variability. On an American Sign Language fingerspelling dataset, RFaNet achieved 1.77% higher classification accuracy than state-of-the-art methods. RFaNet achieved effective transfer learning when the number of labeled depth images was insufficient. The fingerspelling representation of a depth image can be effectively transferred from large- to small-scale datasets via highlighting the finger regions and building inter-finger relations, thereby reducing the requirement for expensive fingerspelling annotations. Full article

(This article belongs to the Special Issue Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications)

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24 pages, 9562 KiB

Open AccessArticle

User Evaluation of a Multi-Platform Digital Storytelling Concept for Cultural Heritage

by Silviu Vert, Diana Andone, Andrei Ternauciuc, Vlad Mihaescu, Oana Rotaru, Muguras Mocofan, Ciprian Orhei and Radu Vasiu

Mathematics 2021, 9(21), 2678; https://0-doi-org.brum.beds.ac.uk/10.3390/math9212678 - 22 Oct 2021

Cited by 11 | Viewed by 2359

Abstract

Digital storytelling platforms have proven to be a great way of bringing cultural heritage closer to people. What lacks is a deeper understanding of the user experience of such systems, especially in multi-platform digital storytelling. For the last three years, we have been developing a project called Spotlight Heritage Timisoara, which is at its core a digital storytelling platform for the city of Timisoara (Romania), soon to be European Capital of Culture in 2023. The project consists of a website, mobile applications, and interactive museographic and street exhibitions. This paper presents a multi-platform usability evaluation study which employed semi-structured interviews, observations, think-aloud protocol, SUS questionnaire, Net Promoter Score and Product Reaction Cards to gather insights from 105 participants and reveal usability problems in the Spotlight Heritage context. We found out that the four platforms, i.e., interactive touchscreen table, desktop/laptop, mobile and Augmented Reality, have very good usability scores, are considered accessible and useful, work seamlessly together, and create user satisfaction and loyalty, across demographic groups, having the potential to bring people closer to cultural heritage. Full article

(This article belongs to the Special Issue Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications)

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24 pages, 23398 KiB

Open AccessArticle

Ubiquitous Computing: Driving in the Intelligent Environment

by Emanuela Bran, Elena Bautu, Dragos Florin Sburlan, Crenguta Madalina Puchianu and Dorin Mircea Popovici

Mathematics 2021, 9(21), 2649; https://0-doi-org.brum.beds.ac.uk/10.3390/math9212649 - 20 Oct 2021

Cited by 5 | Viewed by 2984

Abstract

In the context of hyper-connected cars and a growing heterogeneous digital ecosystem, we wish to make the most of the data available from the various sensors, devices and services that compose the ecosystem, in order to propose a proof of concept in-vehicle system that enhances the driving experience. We focus on improving the driving experience along three main directions, namely: (1) driving and trip planning, (2) health and well-being and (3) social and online activities. We approached the in-vehicle space as a smart interface to the intelligent driving environment. The digital data-producers in the ecosystem of the connected car are sources of raw data of various categories, such as data from the outside world, gathered from sensors or online services, data from the car itself and data from the driver gathered with various mobile and wearable devices, by means of observing his state and by means of his social media and online activity. Data is later processed into three information categories—driving, wellness, and social—and used to provide multi-modal interaction, namely visual, audio and gesture. The system is implemented to act in response to the trafficked information on different levels of autonomy, either in a reactive manner, by simple monitoring, or in a proactive manner. The system is designed to provide an in-vehicle system that assists the driver with planning the travel (Drive panel), by providing a comfortable environment for the driver while monitoring him (Wellness panel), and by adaptively managing interactions with their phone and the digital environment (Social panel). Heuristic evaluation of the system is performed, with respect to guidelines formulated for automated vehicles, and a SWOT analysis of the system is also presented in the paper. Full article

(This article belongs to the Special Issue Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications)

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16 pages, 302 KiB

Open AccessArticle

Virtual Dialogue Assistant for Remote Exams

by Anton Matveev, Olesia Makhnytkina, Yuri Matveev, Aleksei Svischev, Polina Korobova, Alexandr Rybin and Artem Akulov

Mathematics 2021, 9(18), 2229; https://0-doi-org.brum.beds.ac.uk/10.3390/math9182229 - 10 Sep 2021

Cited by 3 | Viewed by 1847

Abstract

A Virtual Dialogue Assistant (VDA) is an automated system intended to provide support for conducting tests and examinations in the context of distant education platforms. Online Distance Learning (ODL) has proven to be a critical part of education systems across the world, particularly during the COVID-19 pandemic. While the core components of ODL are sufficiently researched and developed to become mainstream, there is still a demand for various aspects of traditional classroom learning to be implemented or improved to match the expectations for modern ODL systems. In this work, we take a look at the evaluation of students’ performance. Various forms of testing are often present in ODL systems; however, modern Natural Language Processing (NLP) techniques provide new opportunities to improve this aspect of ODL. In this paper, we present an overview of VDA intended for integration with online education platforms to enhance the process of evaluation of students’ performance. We propose an architecture of such a system, review challenges and solutions for building it, and present examples of solutions for several NLP problems and ways to integrate them into the system. The principal challenge for ODL is accessibility; therefore, proposing an enhancement for ODL systems, we formulate the problem from the point of view of a user interacting with it. In conclusion, we affirm that relying on the advancements in NLP and Machine Learning, the approach we suggest can provide an enhanced experience of evaluation of students’ performance for modern ODL platforms. Full article

(This article belongs to the Special Issue Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications)

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