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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 (31 March 2022) | Viewed by 21409

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Special Issue Editors

Prof. Paolo Bottoni

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Guest Editor

Department of Computer Science, Sapienza University, Rome, Italy
Interests: formal methods in design of interactive systems; visual languages; annotations

Prof. Emanuele Panizzi

E-Mail Website
Guest Editor

Department of Computer Science, Sapienza University, Rome, Italy
Interests: mobile interfaces; crowd-sensing systems; infomobility systems; annotations

Special Issue Information

Dear Colleagues,

The global landscape of digitized services competing for user attention requires much of the interaction to occur without explicit actions on the part of the user. Examples are smart living (from homes to whole cities), recommendations, and automatic driving systems. On the other hand, users want to retain control over critical decisions and on choices in manners of delivery of digitized services, as well as on the information made available to service providers or to third parties. Balancing these two needs will be more and more crucial in the near future and might require integration of techniques of implicit and explicit context-aware interaction, exploiting new technologies such as machine learning, visualization, computer vision, natural language processing, to be made available in a ubiquitous way on a plethora of different connected devices. This Special Issue welcomes original, unpublished research contributions including, but not limited to, methodological studies, design and development of new systems and applications, usability studies, and incorporation of innovative technologies into interactive systems.

Prof. Paolo Bottoni
Prof. Emanuele Panizzi
Guest Editors

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Keywords

Context-aware interaction
machine learning
visualization
computer vision
natural language processing
ubiquitous interaction
personalization
smart environments

Published Papers (6 papers)

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Research

15 pages, 4671 KiB

Open AccessArticle

Analyzing User Digital Emotions from a Holy versus Non-Pilgrimage City in Saudi Arabia on Twitter Platform

by Kashish Ara Shakil, Kahkashan Tabassum, Fawziah S. Alqahtani and Mudasir Ahmad Wani

Appl. Sci. 2021, 11(15), 6846; https://doi.org/10.3390/app11156846 - 25 Jul 2021

Cited by 8 | Viewed by 2381

Abstract

Humans are the product of what society and their environment conditions them into being. People living in metropolitan cities have a very fast-paced life and are constantly exposed to different situations. A social media platform enables individuals to express their emotions and sentiments and thus acts as a reservoir for the digital emotion footprints of its users. This study proposes that the user data available on Twitter has the potential to showcase the contrasting emotions of people residing in a pilgrimage city versus those residing in other, non-pilgrimage areas. We collected the Arabic geolocated tweets of users living in Mecca (holy city) and Riyadh (non-pilgrimage city). The user emotions were classified on the basis of Plutchik’s eight basic emotion categories, Fear, Anger, Sadness, Joy, Surprise, Disgust, Trust, and Anticipation. A new bilingual dictionary, AEELex (Arabic English Emotion Lexicon), was designed to determine emotions derived from user tweets. AEELex has been validated on commonly known and popular lexicons. An emotion analysis revealed that people living in Mecca had more positivity than those residing in Riyadh. Anticipation was the emotion that was dominant or most expressed in both places. However, a larger proportion of users living in Mecca fell under this category. The proposed analysis was an initial attempt toward studying the emotional and behavioral differences between users living in different cities of Saudi Arabia. This study has several other important applications. First, the emotion-based study could contribute to the development of a machine learning-based model for predicting depression in netizens. Second, behavioral appearances mined from the text could benefit efforts to identify the regional location of a particular user. Full article

(This article belongs to the Special Issue Implicit and Explicit Human-Computer Interaction)

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35 pages, 6036 KiB

Open AccessArticle

Design and Applications of GLANCE: GLanceable Alarm Notification for a User Centered Experience

by Laura Tarantino, Daniela Angelucci, Alessandra Bonomo, Annalisa Cardinali and Stefania Di Paolo

Appl. Sci. 2021, 11(2), 669; https://doi.org/10.3390/app11020669 - 12 Jan 2021

Cited by 2 | Viewed by 2007

Abstract

The trade-off between awareness and interruption is a crucial aspect in network fault notifiers: Low severity alarms should not distract operators from other primary tasks, however it might be crucial that operators promptly react to critical notifications. A notification system should hence determine when a particular interruption is appropriate and how it should be presented. In this direction, this paper presents a multistep design path beginning from the objective of designing a proof-of-concept for a glanceable alarm notification component for telecommunication network management systems based on a peripheral display approach. In particular the goal was a notifier guided by severity-based strategies and offering the information expressiveness of a one-notification-at-the-time perspective while enriching it with overview capabilities to guarantee (possibly subliminal) long-term local and global content comprehension and prompt reaction only when the interruption from the foreground task is dictated by the fault severity. A first design macro-phase led to the simple yet effective GLANCE (GLanceable Alarm Notification for a User Centered Experience) model, based on a visual coding technique oriented to comprehension and reaction, and a transition strategy oriented to interruptions and reaction. A second design macro-phase studied the application of GLANCE to a personal customizable multichannel notification tool and to a service-oriented fault monitor for digital terrestrial television broadcasting networks. Full article

(This article belongs to the Special Issue Implicit and Explicit Human-Computer Interaction)

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19 pages, 2640 KiB

Open AccessArticle

Assessment of Implicit and Explicit Measures of Mental Workload in Working Situations: Implications for Industry 4.0

by Michele Mingardi, Patrik Pluchino, Davide Bacchin, Chiara Rossato and Luciano Gamberini

Appl. Sci. 2020, 10(18), 6416; https://doi.org/10.3390/app10186416 - 15 Sep 2020

Cited by 8 | Viewed by 3142

Abstract

Nowadays, in the context of Industry 4.0, advanced working environments aim at achieving a high degree of human–machine collaboration. This phenomenon occurs, on the one hand, through the correct interpretation of operators’ data by machines that can adapt their functioning to support workers, and on the other hand, by ensuring the transparency of the actions of the system itself. This study used an ad hoc system that allowed the co-registration of a set of participants’ implicit and explicit (I/E) data in two experimental conditions that varied in the level of mental workload (MWL). Findings showed that the majority of the considered I/E measures were able to discriminate the different task-related mental demands and some implicit measures were capable of predicting task performance in both tasks. Moreover, self-reported measures showed that participants were aware of such differences in MWL. Finally, the paradigm’s ecology highlights that task and environmental features may affect the reliability of the various I/E measures. Thus, these factors have to be considered in the design and development of advanced adaptive systems within the industrial context. Full article

(This article belongs to the Special Issue Implicit and Explicit Human-Computer Interaction)

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18 pages, 1251 KiB

Open AccessArticle

IGV Short Scale to Assess Implicit Value of Visualizations through Explicit Interaction

by Angela Locoro, Federico Cabitza, Aurelio Ravarini and Paolo Buono

Appl. Sci. 2020, 10(18), 6189; https://doi.org/10.3390/app10186189 - 6 Sep 2020

Cited by 2 | Viewed by 2498

Abstract

This paper reports the assessment of the infographics-value (IGV) short scale, designed to measure the value in the use of infographics. The scale was made to assess the implicit quality dimensions of infographics. These dimensions were experienced during the execution of tasks in a contextualized scenario. Users were asked to retrieve a piece of information by explicitly interacting with the infographics. After usage, they were asked to rate quality dimensions of infographics, namely, usefulness, intuitiveness, clarity, informativity, and beauty; the overall value perceived from interacting with infographics was also included in the survey. Each quality dimension was coded as a six-point rating scale item, with overall value included. The proposed IGV short scale model was validated with 650 people. Our analysis confirmed that all considered dimensions in our scale were independently significant and contributed to assessing the implicit value of infographics. The IGV short scale is a lightweight but exhaustive tool to rapidly assess the implicit value of an explicit interaction with infographics in daily tasks, where value in use is crucial to measuring the situated effectiveness of visual tools. Full article

(This article belongs to the Special Issue Implicit and Explicit Human-Computer Interaction)

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29 pages, 2057 KiB

Open AccessArticle

Service-Aware Interactive Presentation of Items for Decision-Making

by Noemi Mauro, Liliana Ardissono, Sara Capecchi and Rosario Galioto

Appl. Sci. 2020, 10(16), 5599; https://doi.org/10.3390/app10165599 - 12 Aug 2020

Cited by 7 | Viewed by 2177

Abstract

Current information exploration models present low-level features or technical aspects related to the paradigm used to generate results. While this may increase transparency, it does not help the user form a personal opinion about items because it does not describe the overall experience with them. In order to address this issue, we propose the INTERactivE viSualizaTion model (INTEREST) that supports the exploration and analysis of search results by means of a graphical representation of consumer feedback aimed at making the user aware of the service properties in all the stages of fruition, focusing on the data that is most relevant to her/him. INTEREST is based on the Service Journey Maps for the design and description of user experience with services. We applied it to the home booking domain by developing the Apartment Monitoring application that supports overviewing and analyzing online reviews about rented homes. In a user study, we compared the decision-making support provided by our application with that of a baseline model that enables a temporal filtering of consumer feedback. We found out that Apartment Monitoring outperforms the baseline in user experience, user awareness of item properties, and user control during the interaction with the system. In particular, according to the participants of the study, Apartment Monitoring describes the expectations about the homes and it supports their selection in a more effective way than the baseline. These findings encourage moving from a low-level description of item properties to a service-oriented one in order to improve users’ decision-making capabilities. Full article

(This article belongs to the Special Issue Implicit and Explicit Human-Computer Interaction)

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21 pages, 311 KiB

Open AccessArticle

Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits

by Martin Gjoreski, Tine Kolenik, Timotej Knez, Mitja Luštrek, Matjaž Gams, Hristijan Gjoreski and Veljko Pejović

Appl. Sci. 2020, 10(11), 3843; https://doi.org/10.3390/app10113843 - 31 May 2020

Cited by 52 | Viewed by 7196

Abstract

This study introduces two datasets for multimodal research on cognitive load inference and personality traits. Different to other datasets in Affective Computing, which disregard participants’ personality traits or focus only on emotions, stress, or cognitive load from one specific task, the participants in our experiments performed seven different tasks in total. In the first dataset, 23 participants played a varying difficulty (easy, medium, and hard) game on a smartphone. In the second dataset, 23 participants performed six psychological tasks on a PC, again with varying difficulty. In both experiments, the participants filled personality trait questionnaires and marked their perceived cognitive load using NASA-TLX after each task. Additionally, the participants’ physiological response was recorded using a wrist device measuring heart rate, beat-to-beat intervals, galvanic skin response, skin temperature, and three-axis acceleration. The datasets allow multimodal study of physiological responses of individuals in relation to their personality and cognitive load. Various analyses of relationships between personality traits, subjective cognitive load (i.e., NASA-TLX), and objective cognitive load (i.e., task difficulty) are presented. Additionally, baseline machine learning models for recognizing task difficulty are presented, including a multitask learning (MTL) neural network that outperforms single-task neural network by simultaneously learning from the two datasets. The datasets are publicly available to advance the field of cognitive load inference using commercially available devices. Full article

(This article belongs to the Special Issue Implicit and Explicit Human-Computer Interaction)

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