Informatics

18 pages, 1556 KiB

Open AccessArticle

Benchmarking Deep Learning Methods for Behaviour-Based Network Intrusion Detection

by Mário Antunes, Luís Oliveira, Afonso Seguro, João Veríssimo, Ruben Salgado and Tiago Murteira

Informatics 2022, 9(1), 29; https://doi.org/10.3390/informatics9010029 - 20 Mar 2022

Cited by 8 | Viewed by 3854

Abstract

Network security encloses a wide set of technologies dealing with intrusions detection. Despite the massive adoption of signature-based network intrusion detection systems (IDSs), they fail in detecting zero-day attacks and previously unseen vulnerabilities exploits. Behaviour-based network IDSs have been seen as a way [...] Read more.

Network security encloses a wide set of technologies dealing with intrusions detection. Despite the massive adoption of signature-based network intrusion detection systems (IDSs), they fail in detecting zero-day attacks and previously unseen vulnerabilities exploits. Behaviour-based network IDSs have been seen as a way to overcome signature-based IDS flaws, namely through the implementation of machine-learning-based methods, to tolerate new forms of normal network behaviour, and to identify yet unknown malicious activities. A wide set of machine learning methods has been applied to implement behaviour-based IDSs with promising results on detecting new forms of intrusions and attacks. Innovative machine learning techniques have emerged, namely deep-learning-based techniques, to process unstructured data, speed up the classification process, and improve the overall performance obtained by behaviour-based network intrusion detection systems. The use of realistic datasets of normal and malicious networking activities is crucial to benchmark machine learning models, as they should represent real-world networking scenarios and be based on realistic computers network activity. This paper aims to evaluate CSE-CIC-IDS2018 dataset and benchmark a set of deep-learning-based methods, namely convolutional neural networks (CNN) and long short-term memory (LSTM). Autoencoder and principal component analysis (PCA) methods were also applied to evaluate features reduction in the original dataset and its implications in the overall detection performance. The results revealed the appropriateness of using the CSE-CIC-IDS2018 dataset to benchmark supervised deep learning models. It was also possible to evaluate the robustness of using CNN and LSTM methods to detect unseen normal activity and variations of previously trained attacks. The results reveal that feature reduction methods decreased the processing time without loss of accuracy in the overall detection performance. Full article

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24 pages, 628 KiB

Open AccessArticle

Factors Affecting Reputational Damage to Organisations Due to Cyberattacks

by Srinath Perera, Xiaohua Jin, Alana Maurushat and De-Graft Joe Opoku

Informatics 2022, 9(1), 28; https://doi.org/10.3390/informatics9010028 - 18 Mar 2022

Cited by 12 | Viewed by 11726

Abstract

The COVID-19 pandemic has brought massive online activities and increased cybersecurity incidents and cybercrime. As a result of this, the cyber reputation of organisations has also received increased scrutiny and global attention. Due to increased cybercrime, reputation displaying a more important role within [...] Read more.

The COVID-19 pandemic has brought massive online activities and increased cybersecurity incidents and cybercrime. As a result of this, the cyber reputation of organisations has also received increased scrutiny and global attention. Due to increased cybercrime, reputation displaying a more important role within risk management frameworks both within public and private institutions is vital. This study identifies key factors in determining reputational damage to public and private sector institutions through cyberattacks. Researchers conducted an extensive review of the literature, which addresses factors relating to risk management of reputation post-cyber breach. The study identified 42 potential factors, which were then classified using the STAR model. This model is an organisational design framework and was suitable due to its alignment with organisations. A qualitative study using semi-structured and structured questions was conducted with purposively selected cybersecurity experts in both public and private sector institutions. Data obtained from the expert forum were analysed using thematic analysis, which revealed that a commonly accepted definition for cyber reputation was lacking despite the growing use of the term “online reputation”. In addition, the structured questions data were analysed using relative importance index rankings. The analysis results revealed significant factors in determining reputational damage due to cyberattacks, as well as highlighting reputation factor discrepancies between private and public institutions. Theoretically, this study contributes to the body of knowledge relating to cybersecurity of organisations. Practically, this research is expected to aid organisations to properly position themselves to meet cyber incidents and become more competitive in the post-COVID-19 era. Full article

(This article belongs to the Special Issue Feature Papers in Informatics in 2022)

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19 pages, 984 KiB

Open AccessArticle

UTAUT Model for Smart City Concept Implementation: Use of Web Applications by Residents for Everyday Operations

by Yelena Popova and Diana Zagulova

Informatics 2022, 9(1), 27; https://doi.org/10.3390/informatics9010027 - 10 Mar 2022

Cited by 14 | Viewed by 3786

Abstract

The article considers the attitude of smart city residents towards the use of web applications in everyday life. It is very important for many stakeholders since it affects the involvement of people in all processes of urban life and contributes to the implementation [...] Read more.

The article considers the attitude of smart city residents towards the use of web applications in everyday life. It is very important for many stakeholders since it affects the involvement of people in all processes of urban life and contributes to the implementation of the smart city concept. The goal of the research is to study the factors influencing the intention and use of web applications in a smart city. Based on the results of surveying the residents of Riga, the UTA UT model was applied with the employment of partial least squares structural equation modeling in Smart PLS. The traditional constructs of the UTAUT model—Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), as well as Attitude towards the use of Applications (ATA)—had a direct or indirect positive relationship with the intention to use technologies (Behavioral Intention: BI) and/or with usage of these technologies (Use Behavior: UB). Anxiety indirectly via ATA showed a negative effect on UB. The influence of Age, Gender and Education on BI and UB as moderators was also investigated. Only Age as a moderator negatively affected the relationship between FC and PE and SI. The results showed that in order to involve in full scope of the population of Riga in the use of communication technologies and the implementation of the smart city concept, it is necessary to create the appropriate conditions for residents, in particular by teaching people on a permanent basis. Some of the obtained results were different from similar studies’ results, which emphasizes that city authorities and other stakeholders should make decisions on the involvement of citizens in smart process based on the local peculiarities, which supports the slogan of smart cities—think globally, act locally. Full article

(This article belongs to the Special Issue Building Smart Cities and Infrastructures for a Sustainable Future)

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15 pages, 866 KiB

Open AccessArticle

Where Is My Mind (Looking at)? A Study of the EEG–Visual Attention Relationship

by Victor Delvigne, Noé Tits, Luca La Fisca, Nathan Hubens, Antoine Maiorca, Hazem Wannous, Thierry Dutoit and Jean-Philippe Vandeborre

Informatics 2022, 9(1), 26; https://doi.org/10.3390/informatics9010026 - 09 Mar 2022

Cited by 1 | Viewed by 3882

Abstract

Visual attention estimation is an active field of research at the crossroads of different disciplines: computer vision, deep learning, and medicine. One of the most common approaches to estimate a saliency map representing attention is based on the observed images. In this paper, [...] Read more.

Visual attention estimation is an active field of research at the crossroads of different disciplines: computer vision, deep learning, and medicine. One of the most common approaches to estimate a saliency map representing attention is based on the observed images. In this paper, we show that visual attention can be retrieved from EEG acquisition. The results are comparable to traditional predictions from observed images, which is of great interest. Image-based saliency estimation being participant independent, the estimation from EEG could take into account the subject specificity. For this purpose, a set of signals has been recorded, and different models have been developed to study the relationship between visual attention and brain activity. The results are encouraging and comparable with other approaches estimating attention with other modalities. Being able to predict a visual saliency map from EEG could help in research studying the relationship between brain activity and visual attention. It could also help in various applications: vigilance assessment during driving, neuromarketing, and also in the help for the diagnosis and treatment of visual attention-related diseases. For the sake of reproducibility, the codes and dataset considered in this paper have been made publicly available to promote research in the field. Full article

(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)

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32 pages, 7175 KiB

Open AccessArticle

“Saving Precious Seconds”—A Novel Approach to Implementing a Low-Cost Earthquake Early Warning System with Node-Level Detection and Alert Generation

by Raj Prasanna, Chanthujan Chandrakumar, Rasika Nandana, Caroline Holden, Amal Punchihewa, Julia S. Becker, Seokho Jeong, Nandika Liyanage, Danuka Ravishan, Rangana Sampath and Marion Lara Tan

Informatics 2022, 9(1), 25; https://doi.org/10.3390/informatics9010025 - 08 Mar 2022

Cited by 19 | Viewed by 7220

Abstract

This paper presents findings from ongoing research that explores the ability to use Micro-Electromechanical Systems (MEMS)-based technologies and various digital communication protocols for earthquake early warning (EEW). The paper proposes a step-by-step guide to developing a unique EEW network architecture driven by a [...] Read more.

This paper presents findings from ongoing research that explores the ability to use Micro-Electromechanical Systems (MEMS)-based technologies and various digital communication protocols for earthquake early warning (EEW). The paper proposes a step-by-step guide to developing a unique EEW network architecture driven by a Software-Defined Wide Area Network (SD-WAN)-based hole-punching technology consisting of MEMS-based, low-cost accelerometers hosted by the general public. In contrast with most centralised cloud-based approaches, a node-level decentralised data-processing is used to generate warnings with the support of a modified Propagation of Local Undamped Motion (PLUM)-based EEW algorithm. With several hypothetical earthquake scenarios, experiments were conducted to evaluate the system latencies of the proposed decentralised EEW architecture and its performance was compared with traditional centralised EEW architecture. The results from sixty simulations show that the SD-WAN-based hole-punching architecture supported by the Transmission Control Protocol (TCP) creates the optimum alerting conditions. Furthermore, the results provide clear evidence to show that the decentralised EEW system architecture can outperform the centralised EEW architecture and can save valuable seconds when generating EEW, leading to a longer warning time for the end-user. This paper contributes to the EEW literature by proposing a novel EEW network architecture. Full article

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22 pages, 388 KiB

Open AccessArticle

Modeling Subcutaneous Microchip Implant Acceptance in the General Population: A Cross-Sectional Survey about Concerns and Expectations

by Shekufeh Shafeie, Beenish Moalla Chaudhry and Mona Mohamed

Informatics 2022, 9(1), 24; https://doi.org/10.3390/informatics9010024 - 07 Mar 2022

Cited by 8 | Viewed by 9555

Abstract

Despite the numerous advantages of microchip implants, their adoption remains low in the public sector. We conducted a cross-sectional survey to identify concerns and expectations about microchip implants among potential users. A total of 179 United States adults aged 18–83 years responded to [...] Read more.

Despite the numerous advantages of microchip implants, their adoption remains low in the public sector. We conducted a cross-sectional survey to identify concerns and expectations about microchip implants among potential users. A total of 179 United States adults aged 18–83 years responded to two qualitative questions that were then analyzed using the thematic analysis technique. The identified codes were first categorized and then clustered to generate themes for both concerns and expectations. The prevalence of each theme was calculated across various demographic factors. Concerns were related to data protection, health risks, knowledge, negative affect, ease of use, metaphysical dilemmas, monetary issues, and negative social impact. Expectations included medical and non-medical uses, dismissal of microchips, technical advances, human enhancement, regulations, and affordability. The prevalence of concerns and benefits differed by immigration status and medical conditions. Informed by our findings, we present a modification to the Technology Acceptance Model for predicting public’s behavioral intention to use subcutaneous microchips. We discuss the five newly proposed determinants and seven predictor variables of this model by surveying the literature. Full article

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14 pages, 5251 KiB

Open AccessFeature PaperArticle

Identifying Faked Responses in Questionnaires with Self-Attention-Based Autoencoders

by Alberto Purpura, Giuseppe Sartori, Dora Giorgianni, Graziella Orrú and Gian Antonio Susto

Informatics 2022, 9(1), 23; https://doi.org/10.3390/informatics9010023 - 06 Mar 2022

Cited by 1 | Viewed by 3389

Abstract

Deception, also known as faking, is a critical issue when collecting data using questionnaires. As shown by previous studies, people have the tendency to fake their answers whenever they gain an advantage from doing so, e.g., when taking a test for a job [...] Read more.

Deception, also known as faking, is a critical issue when collecting data using questionnaires. As shown by previous studies, people have the tendency to fake their answers whenever they gain an advantage from doing so, e.g., when taking a test for a job application. Current methods identify the general attitude of faking but fail to identify faking patterns and the exact responses affected. Moreover, these strategies often require extensive data collection of honest responses and faking patterns related to the specific questionnaire use case, e.g., the position that people are applying to. In this work, we propose a self-attention-based autoencoder (SABA) model that can spot faked responses in a questionnaire solely relying on a set of honest answers that are not necessarily related to its final use case. We collect data relative to a popular personality test (the 10-item Big Five test) in three different use cases, i.e., to obtain: (i) child custody in court, (ii) a position as a salesperson, and (iii) a role in a humanitarian organization. The proposed model outperforms by a sizeable margin in terms of F1 score three competitive baselines, i.e., an autoencoder based only on feedforward layers, a distribution model, and a k-nearest-neighbor-based model. Full article

(This article belongs to the Special Issue Exclusive Papers Collection of Editorial Board Members and Scholars Invited by Editorial Board Members of Informatics)

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14 pages, 564 KiB

Open AccessFeature PaperArticle

Social Media and Social Support: A Framework for Patient Satisfaction in Healthcare

by Md Irfanuzzaman Khan, Zoeb Ur Rahman, M. Abu Saleh and Saeed Uz Zaman Khan

Informatics 2022, 9(1), 22; https://doi.org/10.3390/informatics9010022 - 04 Mar 2022

Cited by 4 | Viewed by 5362

Abstract

Social media has been a powerful source of social support for health consumers. In the healthcare sector, social media has thrived, building on various dynamic platforms supporting the connection between social relationships, health, and wellbeing. While prior research has shown that social support [...] Read more.

Social media has been a powerful source of social support for health consumers. In the healthcare sector, social media has thrived, building on various dynamic platforms supporting the connection between social relationships, health, and wellbeing. While prior research has shown that social support exerts a positive impact on health outcomes, there is scant literature examining the implications of social support for patient satisfaction, which suggests that there is a profound gap in the extant literature. The objective of this study is to develop and test a theoretical model for understanding the relationship between different dimensions of social support and patient empowerment. The study further investigates the debated relationship between patient empowerment and patient satisfaction. The measurement model indicated an acceptable fit (χ² = 260.226; df, 107, χ²/df = 2.432, RMSEA = 0.07, GFI = 0.90, IFI = 0.95, TLI = 0.94, and CFI = 0.95). Findings indicate that emotional support (p < 0.001), information support (p < 0.05), and network support (p < 0.001) positively influence the notion of patient empowerment. In turn, patient empowerment positively influences patient satisfaction (p < 0.001). The proposed framework contributes to the health communication literature by introducing a novel framework for patient satisfaction in the social media context, which provides important inputs for healthcare service providers in developing patient empowerment strategies. Full article

(This article belongs to the Special Issue Feature Papers: Health Informatics)

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14 pages, 1554 KiB

Open AccessArticle

Identification of Bots and Cyborgs in the #FeesMustFall Campaign

by Yaseen Khan, Surendra Thakur, Obiseye Obiyemi and Emmanuel Adetiba

Informatics 2022, 9(1), 21; https://doi.org/10.3390/informatics9010021 - 04 Mar 2022

Cited by 2 | Viewed by 2725

Abstract

Bots (social robots) are computer programs that replicate human behavior in online social networks. They are either fully automated or semi-automated, and their use makes online activism vulnerable to manipulation. This study examines the existence of social robots in the #FeesMustFall movement by [...] Read more.

Bots (social robots) are computer programs that replicate human behavior in online social networks. They are either fully automated or semi-automated, and their use makes online activism vulnerable to manipulation. This study examines the existence of social robots in the #FeesMustFall movement by conducting a scientific investigation into whether social bots were present in the form of Twitter bots and cyborgs. A total of 576,823 tweets posted between 15 October 2015 and 10 April 2017 were cleaned, with 490,449 tweets analyzed for 90,783 unique persons. Three separate approaches were used to screen out suspicious bot and cyborg activity, supplemented by the DeBot team’s methodology. User 1 and User 2, two of the 90,783 individuals, were recognized as bots or cyborgs in the study and contributed 22,413 (4.57 percent) of the 490,449 tweets. This confirms the existence of bots throughout the campaign, which aided in the #FeesMustFall’s amplification on Twitter, complicating sentiment analysis and invariably making it the most popular and lengthiest hashtag campaign in Africa, particularly at the time of data collection. Full article

(This article belongs to the Section Human-Computer Interaction)

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14 pages, 1091 KiB

Open AccessArticle

Adoption of Large-Scale Scrum Practices through the Use of Management 3.0

by Fernando Almeida and Eduardo Espinheira

Informatics 2022, 9(1), 20; https://doi.org/10.3390/informatics9010020 - 04 Mar 2022

Cited by 10 | Viewed by 4325

Abstract

Software engineering companies have progressively incorporated agile project management methodologies. Initially, this migration occurred mostly in the context of startups, but in recent years it has also sparked interest from other companies with larger and more geographically dispersed teams. One of the frameworks [...] Read more.

Software engineering companies have progressively incorporated agile project management methodologies. Initially, this migration occurred mostly in the context of startups, but in recent years it has also sparked interest from other companies with larger and more geographically dispersed teams. One of the frameworks used for large-scale agile implementation is the LeSS framework. This study seeks to explore how Management 3.0 principles can be applied in the context of the ten practices proposed in the LeSS framework. To this end, a qualitative research methodology based on four case studies is used to identify and explore the role of Management 3.0 in software management and development processes that adopt this agile paradigm. The findings show that the principles of Management 3.0 are relevant to the implementation of the LeSS framework practices, especially in fostering team values and personal values; however, distinct principles between the two paradigms are also identified, namely the greater rigidity of processes advocated in the LeSS framework and a greater focus on process automation. Full article

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17 pages, 306 KiB

Open AccessFeature PaperArticle

The Future of Accounting: How Will Digital Transformation Impact the Sector?

by Maria José Angélico Gonçalves, Amélia Cristina Ferreira da Silva and Carina Gonçalves Ferreira

Informatics 2022, 9(1), 19; https://doi.org/10.3390/informatics9010019 - 27 Feb 2022

Cited by 35 | Viewed by 26013

Abstract

The growing dissemination of digital technologies has had an incomparable impact on many dimensions of today’s civilisation. Digital transformation (DT) redefined the industrial structures and reinvented business models. Hence, in the face of Industry 4.0, financial and accounting services face new threats, challenges, [...] Read more.

The growing dissemination of digital technologies has had an incomparable impact on many dimensions of today’s civilisation. Digital transformation (DT) redefined the industrial structures and reinvented business models. Hence, in the face of Industry 4.0, financial and accounting services face new threats, challenges, and opportunities. How do the business players in the accounting sector perceive this phenomenon? This paper aims to answer this question by following a qualitative and exploratory approach, applied to three case studies, using semi-structured interviews. The study shows that although digital transformation in Portuguese small and medium-sized accounting service enterprises is just starting, Industry 4.0 technologies, optical character recognition (OCR), artificial intelligence (AI), robotics and enterprise resource planning (ERP) in the cloud were the technologies singled out by respondents. Resistance to change, organisational culture and price seem to be the main barriers to DT in accounting. This paper contributes to a better understanding of the role of accounting and accountants in organisations and society in the context of the digital era. Moreover, it provides practical insights into the potential relationship between technological (specifically digital) development and labour market dynamics for accounting professionals. Full article

(This article belongs to the Special Issue Digitalisation, Green Deal and Sustainability)

25 pages, 2810 KiB

Open AccessArticle

Automatic Ethnicity Classification from Middle Part of the Face Using Convolutional Neural Networks

by David Belcar, Petra Grd and Igor Tomičić

Informatics 2022, 9(1), 18; https://doi.org/10.3390/informatics9010018 - 25 Feb 2022

Cited by 11 | Viewed by 4396

Abstract

In the field of face biometrics, finding the identity of a person in an image is most researched, but there are other, soft biometric information that are equally as important, such as age, gender, ethnicity or emotion. Nowadays, ethnicity classification has a wide [...] Read more.

In the field of face biometrics, finding the identity of a person in an image is most researched, but there are other, soft biometric information that are equally as important, such as age, gender, ethnicity or emotion. Nowadays, ethnicity classification has a wide application area and is a prolific area of research. This paper gives an overview of recent advances in ethnicity classification with focus on convolutional neural networks (CNNs) and proposes a new ethnicity classification method using only the middle part of the face and CNN. The paper also compares the differences in results of CNN with and without plotted landmarks. The proposed model was tested using holdout testing method on UTKFace dataset and FairFace dataset. The accuracy of the model was 80.34% for classification into five classes and 61.74% for classification into seven classes, which is slightly better than state-of-the-art, but it is also important to note that results in this paper are obtained by using only the middle part of the face which reduces the time and resources necessary. Full article

(This article belongs to the Section Machine Learning)

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28 pages, 3344 KiB

Open AccessEditor’s ChoiceArticle

Visual Analytics for Predicting Disease Outcomes Using Laboratory Test Results

by Neda Rostamzadeh, Sheikh S. Abdullah, Kamran Sedig, Amit X. Garg and Eric McArthur

Informatics 2022, 9(1), 17; https://doi.org/10.3390/informatics9010017 - 25 Feb 2022

Viewed by 3046

Abstract

Laboratory tests play an essential role in the early and accurate diagnosis of diseases. In this paper, we propose SUNRISE, a visual analytics system that allows the user to interactively explore the relationships between laboratory test results and a disease outcome. SUNRISE integrates [...] Read more.

Laboratory tests play an essential role in the early and accurate diagnosis of diseases. In this paper, we propose SUNRISE, a visual analytics system that allows the user to interactively explore the relationships between laboratory test results and a disease outcome. SUNRISE integrates frequent itemset mining (i.e., Eclat algorithm) with extreme gradient boosting (XGBoost) to develop more specialized and accurate prediction models. It also includes interactive visualizations to allow the user to interact with the model and track the decision process. SUNRISE helps the user probe the prediction model by generating input examples and observing how the model responds. Furthermore, it improves the user’s confidence in the generated predictions and provides them the means to validate the model’s response by illustrating the underlying working mechanism of the prediction models through visualization representations. SUNRISE offers a balanced distribution of processing load through the seamless integration of analytical methods with interactive visual representations to support the user’s cognitive tasks. We demonstrate the usefulness of SUNRISE through a usage scenario of exploring the association between laboratory test results and acute kidney injury, using large provincial healthcare databases from Ontario, Canada. Full article

(This article belongs to the Special Issue Feature Papers: Health Informatics)

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21 pages, 2229 KiB

Open AccessArticle

BYOD Security: A Study of Human Dimensions

by Kathleen Downer and Maumita Bhattacharya

Informatics 2022, 9(1), 16; https://doi.org/10.3390/informatics9010016 - 23 Feb 2022

Cited by 7 | Viewed by 6416

Abstract

The prevalence and maturity of Bring Your Own Device (BYOD) security along with subsequent frameworks and security mechanisms in Australian organisations is a growing phenomenon somewhat similar to other developed nations. During the COVID-19 pandemic, even organisations that were previously reluctant to embrace [...] Read more.

The prevalence and maturity of Bring Your Own Device (BYOD) security along with subsequent frameworks and security mechanisms in Australian organisations is a growing phenomenon somewhat similar to other developed nations. During the COVID-19 pandemic, even organisations that were previously reluctant to embrace BYOD have been forced to accept it to facilitate remote work. The aim of this paper is to discover, through a study conducted using a survey questionnaire instrument, how employees practice and perceive the BYOD security mechanisms deployed by Australian businesses which can help guide the development of future BYOD security frameworks. Three research questions are answered by this study: What levels of awareness do Australian businesses have for BYOD security aspects? How are employees currently responding to the security mechanisms applied by their organisations for mobile devices? What are the potential weaknesses in businesses’ IT networks that have a direct effect on BYOD security? Overall, the aim of this research is to illuminate the findings of these research objectives so that they can be used as a basis for building new and strengthening existing BYOD security frameworks in order to enhance their effectiveness against an ever-growing list of attacks and threats targeting mobile devices in a virtually driven work force. Full article

(This article belongs to the Section Social Informatics and Digital Humanities)

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15 pages, 2268 KiB

Open AccessArticle

Raising Awareness of Smartphone Overuse among University Students: A Persuasive Systems Approach

by Carlos Abreu and Pedro F. Campos

Informatics 2022, 9(1), 15; https://doi.org/10.3390/informatics9010015 - 23 Feb 2022

Cited by 3 | Viewed by 4668

Abstract

Smartphone overuse can lead to a series of physical, mental and social disturbances. This problem is more prevalent among young adults as compared to other demographic groups. Additionally, university students are already undergoing high cognitive loads and stress conditions; therefore, they are more [...] Read more.

Smartphone overuse can lead to a series of physical, mental and social disturbances. This problem is more prevalent among young adults as compared to other demographic groups. Additionally, university students are already undergoing high cognitive loads and stress conditions; therefore, they are more susceptible to smartphone addiction and its derived problems. In this paper, we present a novel approach where a conversational mobile agent uses persuasive messages exploring the reflective mind to raise users’ awareness of their usage and consequently induce reduction behaviors. We conducted a four-week study with 16 university students undergoing stressful conditions—a global lockdown during their semester—and evaluated the impact of the agent on smartphone usage reduction and the perceived usefulness of such an approach. Results show the efficacy of self-tracking in the behavior change process: 81% of the users reduced their usage time, and all of them mentioned that having a conversational agent alerting them about their usage was useful. Before this experiment, only 68% of them considered such an approach could be useful. In conclusion, users deemed it essential to have an engaging conversational agent on their smartphones, in terms of helping them become more aware of usage times. Full article

(This article belongs to the Special Issue Feature Papers in Human-Computer Interaction)

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18 pages, 378 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Human-Computer Interaction in Digital Mental Health

by Luke Balcombe and Diego De Leo

Informatics 2022, 9(1), 14; https://doi.org/10.3390/informatics9010014 - 22 Feb 2022

Cited by 19 | Viewed by 24780

Abstract

Human-computer interaction (HCI) has contributed to the design and development of some efficient, user-friendly, cost-effective, and adaptable digital mental health solutions. But HCI has not been well-combined into technological developments resulting in quality and safety concerns. Digital platforms and artificial intelligence (AI) have [...] Read more.

Human-computer interaction (HCI) has contributed to the design and development of some efficient, user-friendly, cost-effective, and adaptable digital mental health solutions. But HCI has not been well-combined into technological developments resulting in quality and safety concerns. Digital platforms and artificial intelligence (AI) have a good potential to improve prediction, identification, coordination, and treatment by mental health care and suicide prevention services. AI is driving web-based and smartphone apps; mostly it is used for self-help and guided cognitive behavioral therapy (CBT) for anxiety and depression. Interactive AI may help real-time screening and treatment in outdated, strained or lacking mental healthcare systems. The barriers for using AI in mental healthcare include accessibility, efficacy, reliability, usability, safety, security, ethics, suitable education and training, and socio-cultural adaptability. Apps, real-time machine learning algorithms, immersive technologies, and digital phenotyping are notable prospects. Generally, there is a need for faster and better human factors in combination with machine interaction and automation, higher levels of effectiveness evaluation and the application of blended, hybrid or stepped care in an adjunct approach. HCI modeling may assist in the design and development of usable applications, and to effectively recognize, acknowledge, and address the inequities of mental health care and suicide prevention and assist in the digital therapeutic alliance. Full article

(This article belongs to the Special Issue Feature Papers in Human-Computer Interaction)

32 pages, 1930 KiB

Open AccessSystematic Review

Systematic Review of Multimodal Human–Computer Interaction

by Jose Daniel Azofeifa, Julieta Noguez, Sergio Ruiz, José Martín Molina-Espinosa, Alejandra J. Magana and Bedrich Benes

Informatics 2022, 9(1), 13; https://doi.org/10.3390/informatics9010013 - 15 Feb 2022

Cited by 11 | Viewed by 7508

Abstract

This document presents a systematic review of Multimodal Human–Computer Interaction. It shows how different types of interaction technologies (virtual reality (VR) and augmented reality, force and vibration feedback devices (haptics), and tracking) are used in different domains (concepts, medicine, physics, human factors/user experience [...] Read more.

This document presents a systematic review of Multimodal Human–Computer Interaction. It shows how different types of interaction technologies (virtual reality (VR) and augmented reality, force and vibration feedback devices (haptics), and tracking) are used in different domains (concepts, medicine, physics, human factors/user experience design, transportation, cultural heritage, and industry). A systematic literature search was conducted identifying 406 articles initially. From these articles, we selected 112 research works that we consider most relevant for the content of this article. The articles were analyzed in-depth from the viewpoint of temporal patterns, frequency of usage in types of technology in different domains, and cluster analysis. The analysis allowed us to answer relevant questions in searching for the next steps in work related to multimodal HCI. We looked at the typical technology type, how the technology type and frequency have changed in time over each domain, and how papers are grouped across metrics given their similarities. This analysis determined that VR and haptics are the most widely used in all domains. While VR is the most used, haptic interaction is presented in an increasing number of applications, suggesting future work on applications that configure VR and haptic together. Full article

(This article belongs to the Section Human-Computer Interaction)

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21 pages, 979 KiB

Open AccessArticle

E-MDAV: A Framework for Developing Data-Intensive Web Applications

by Paolo Bocciarelli, Andrea D’Ambrogio, Tommaso Panetti and Andrea Giglio

Informatics 2022, 9(1), 12; https://doi.org/10.3390/informatics9010012 - 12 Feb 2022

Cited by 3 | Viewed by 2469

Abstract

The ever-increasing adoption of innovative technologies, such as big data and cloud computing, provides significant opportunities for organizations operating in the IT domain, but also introduces considerable challenges. Such innovations call for development processes that better align with stakeholders needs and expectations. In [...] Read more.

The ever-increasing adoption of innovative technologies, such as big data and cloud computing, provides significant opportunities for organizations operating in the IT domain, but also introduces considerable challenges. Such innovations call for development processes that better align with stakeholders needs and expectations. In this respect, this paper introduces a development framework based on the OMG’s Model Driven Architecture (MDA) that aims to support the development lifecycle of data-intensive web applications. The proposed framework, named E-MDAV (Extended MDA-VIEW), defines a methodology that exploits a chain of model transformations to effectively cope with both forward- and reverse-engineering aspects. In addition, E-MDAV includes the specification of a reference architecture for driving the implementation of a tool that supports the various professional roles involved in the development and maintenance of data-intensive web applications. In order to evaluate the effectiveness of the proposed E-MDAV framework, a tool prototype has been developed. E-MDAV has then been applied to two different application scenarios and the obtained results have been compared with historical data related to the implementation of similar development projects, in order to measure and discuss the benefits of the proposed approach. Full article

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12 pages, 1935 KiB

Open AccessArticle

Modelling Service Quality of Internet Service Providers during COVID-19: The Customer Perspective Based on Twitter Dataset

by Bagus Setya Rintyarna, Heri Kuswanto, Riyanarto Sarno, Emy Kholifah Rachmaningsih, Fika Hastarita Rachman, Wiwik Suharso and Triawan Adi Cahyanto

Informatics 2022, 9(1), 11; https://doi.org/10.3390/informatics9010011 - 29 Jan 2022

Cited by 3 | Viewed by 4019

Abstract

Internet service providers (ISPs) conduct their business by providing Internet access features to their customers. The COVID-19 pandemic has shifted most activity being performed remotely using an Internet connection. As a result, the demand for Internet services increased by 50%. This significant rise [...] Read more.

Internet service providers (ISPs) conduct their business by providing Internet access features to their customers. The COVID-19 pandemic has shifted most activity being performed remotely using an Internet connection. As a result, the demand for Internet services increased by 50%. This significant rise in the appeal of Internet services needs to be overtaken by a notable increase in the service quality provided by ISPs. Service quality plays a great role for enterprises, including ISPs, in retaining consumer loyalty. Thus, modelling ISPs’ service quality is of great importance. Since a common technique to reveal service quality is a timely and costly pencil survey-based method, this work proposes a framework based on the Sentiment Analysis (SA) of the Twitter dataset to model service quality. The SA involves the majority voting of three machine learning algorithms namely Naïve Bayes, Multinomial Naïve Bayes and Bernoulli Naïve Bayes. Making use of Thaicon’s service quality metrics, this work proposes a formula to generate a rating of service quality accordingly. For the case studies, we examined two ISPs in Indonesia, i.e., By.U and MPWR. The framework successfully extracted the service quality rate of both ISPs, revealing that By.U is better in terms of service quality, as indicated by a service quality rate of 0.71. Meanwhile, MPWR outperforms By.U in terms of customer service. Full article

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24 pages, 23419 KiB

Open AccessArticle

Using the Unity Game Engine to Develop a 3D Simulated Ecological System Based on a Predator–Prey Model Extended by Gene Evolution

by Attila Kiss and Gábor Pusztai

Informatics 2022, 9(1), 9; https://doi.org/10.3390/informatics9010009 - 26 Jan 2022

Viewed by 6010

Abstract

In this paper, we present a novel implementation of an ecosystem simulation. In our previous work, we implemented a 3D environment based on a predator–prey model, but we found that in most cases, regardless of the choice of starting parameters, the simulation quickly [...] Read more.

In this paper, we present a novel implementation of an ecosystem simulation. In our previous work, we implemented a 3D environment based on a predator–prey model, but we found that in most cases, regardless of the choice of starting parameters, the simulation quickly led to extinctions. We wanted to achieve system stabilization, long-term operation, and better simulation of reality by incorporating genetic evolution. Therefore we applied the predator–prey model with an evolutional approach. Using the Unity game engine we created and managed a closed 3D ecosystem environment defined by an artificial or real uploaded map. We present some demonstrative runs while gathering data, observing interesting events (such as extinction, sustainability, and behavior of swarms), and analyzing possible effects on the initial parameters of the system. We found that incorporating genetic evolution into the simulation slightly stabilized the system, thus reducing the likelihood of extinction of different types of objects. The simulation of ecosystems and the analysis of the data generated during the simulations can also be a starting point for further research, especially in relation to sustainability. Our system is publicly available, so anyone can customize and upload their own parameters, maps, objects, and biological species, as well as inheritance and behavioral habits, so they can test their own hypotheses from the data generated during its operation. The goal of this article was not to create and validate a model but to create an IT tool for evolutionary researchers who want to test their own models and to present them, for example, as animated conference presentations. The use of 3D simulation is primarily useful for educational purposes, such as to engage students and to increase their interest in biology. Students can learn in a playful way while observing in the graphical scenery how the ecosystem behaves, how natural selection helps the adaptability and survival of species, and what effects overpopulation and competition can have. Full article

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11 pages, 1651 KiB

Open AccessArticle

Predictive Model for ICU Readmission Based on Discharge Summaries Using Machine Learning and Natural Language Processing

by Negar Orangi-Fard, Alireza Akhbardeh and Hersh Sagreiya

Informatics 2022, 9(1), 10; https://doi.org/10.3390/informatics9010010 - 26 Jan 2022

Cited by 4 | Viewed by 4281

Abstract

Predicting ICU readmission risk will help physicians make decisions regarding discharge. We used discharge summaries to predict ICU 30-day readmission risk using text mining and machine learning (ML) with data from the Medical Information Mart for Intensive Care III (MIMIC-III). We used Natural [...] Read more.

Predicting ICU readmission risk will help physicians make decisions regarding discharge. We used discharge summaries to predict ICU 30-day readmission risk using text mining and machine learning (ML) with data from the Medical Information Mart for Intensive Care III (MIMIC-III). We used Natural Language Processing (NLP) and the Bag-of-Words approach on discharge summaries to build a Document-Term-Matrix with 3000 features. We compared the performance of support vector machines with the radial basis function kernel (SVM-RBF), adaptive boosting (AdaBoost), quadratic discriminant analysis (QDA), least absolute shrinkage and selection operator (LASSO), and Ridge Regression. A total of 4000 patients were used for model training and 6000 were used for validation. Using the bag-of-words determined by NLP, the area under the receiver operating characteristic (AUROC) curve was 0.71, 0.68, 0.65, 0.69, and 0.65 correspondingly for SVM-RBF, AdaBoost, QDA, LASSO, and Ridge Regression. We then used the SVM-RBF model for feature selection by incrementally adding features to the model from 1 to 3000 bag-of-words. Through this exhaustive search approach, only 825 features (words) were dominant. Using those selected features, we trained and validated all ML models. The AUROC curve was 0.74, 0.69, 0.67, 0.70, and 0.71 respectively for SVM-RBF, AdaBoost, QDA, LASSO, and Ridge Regression. Overall, this technique could predict ICU readmission relatively well. Full article

(This article belongs to the Section Medical and Clinical Informatics)

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17 pages, 4802 KiB

Open AccessArticle

What Features of Ligands Are Relevant to the Opening of Cryptic Pockets in Drug Targets?

by Zhonghua Xia, Pavel Karpov, Grzegorz Popowicz, Michael Sattler and Igor V. Tetko

Informatics 2022, 9(1), 8; https://doi.org/10.3390/informatics9010008 - 25 Jan 2022

Cited by 2 | Viewed by 3025

Abstract

Small-molecule drug design aims to identify inhibitors that can specifically bind to a functionally important region on the target, i.e., an active site of an enzyme. Identification of potential binding pockets is typically based on static three-dimensional structures. However, small molecules may induce [...] Read more.

Small-molecule drug design aims to identify inhibitors that can specifically bind to a functionally important region on the target, i.e., an active site of an enzyme. Identification of potential binding pockets is typically based on static three-dimensional structures. However, small molecules may induce and select a dynamic binding pocket that is not visible in the apo protein, which presents a well-recognized challenge for structure-based drug discovery. Here, we assessed whether it is possible to identify features in molecules, which we refer to as inducers, that can induce the opening of cryptic pockets. The volume change between apo and bound protein conformations was used as a metric to differentiate chemical features in inducers vs. non-inducers. Based on the dataset of holo–apo pairs, classification models were built to determine an optimum threshold. The model analysis suggested that inducers preferred to be more hydrophobic and aromatic. The impact of sulfur was ambiguous, while phosphorus and halogen atoms were overrepresented in inducers. The fragment analysis showed that small changes in the structures of molecules can strongly affect the potential to induce a cryptic pocket. This analysis and developed model can be used to design inducers that can potentially open cryptic pockets for undruggable proteins. Full article

(This article belongs to the Special Issue Exclusive Papers Collection of Editorial Board Members and Scholars Invited by Editorial Board Members of Informatics)

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3 pages, 311 KiB

Open AccessEditorial

Acknowledgment to Reviewers of Informatics in 2021

by Informatics Editorial Office

Informatics 2022, 9(1), 7; https://doi.org/10.3390/informatics9010007 - 24 Jan 2022

Viewed by 1796

Abstract

Rigorous peer-reviews are the basis of high-quality academic publishing [...] Full article

29 pages, 5718 KiB

Open AccessArticle

Transferrable Framework Based on Knowledge Graphs for Generating Explainable Results in Domain-Specific, Intelligent Information Retrieval

by Hasan Abu-Rasheed, Christian Weber, Johannes Zenkert, Mareike Dornhöfer and Madjid Fathi

Informatics 2022, 9(1), 6; https://doi.org/10.3390/informatics9010006 - 19 Jan 2022

Cited by 1 | Viewed by 4160

Abstract

In modern industrial systems, collected textual data accumulates over time, offering an important source of information for enhancing present and future industrial practices. Although many AI-based solutions have been developed in the literature for a domain-specific information retrieval (IR) from this data, the [...] Read more.

In modern industrial systems, collected textual data accumulates over time, offering an important source of information for enhancing present and future industrial practices. Although many AI-based solutions have been developed in the literature for a domain-specific information retrieval (IR) from this data, the explainability of these systems was rarely investigated in such domain-specific environments. In addition to considering the domain requirements within an explainable intelligent IR, transferring the explainable IR algorithm to other domains remains an open-ended challenge. This is due to the high costs, which are associated with intensive customization and required knowledge modelling, when developing new explainable solutions for each industrial domain. In this article, we present a transferable framework for generating domain-specific explanations for intelligent IR systems. The aim of our work is to provide a comprehensive approach for constructing explainable IR and recommendation algorithms, which are capable of adopting to domain requirements and are usable in multiple domains at the same time. Our method utilizes knowledge graphs (KG) for modeling the domain knowledge. The KG provides a solid foundation for developing intelligent IR solutions. Utilizing the same KG, we develop graph-based components for generating textual and visual explanations of the retrieved information, taking into account the domain requirements and supporting the transferability to other domain-specific environments, through the structured approach. The use of the KG resulted in minimum-to-zero adjustments when creating explanations for multiple intelligent IR algorithms in multiple domains. We test our method within two different use cases, a semiconductor manufacturing centered use case and a job-to-applicant matching one. Our quantitative results show a high capability of our approach to generate high-level explanations for the end users. In addition, the developed explanation components were highly adaptable to both industrial domains without sacrificing the overall accuracy of the intelligent IR algorithm. Furthermore, a qualitative user-study was conducted. We recorded a high level of acceptance from the users, who reported an enhanced overall experience with the explainable IR system. Full article

(This article belongs to the Special Issue Feature Paper in Informatics)

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18 pages, 860 KiB

Open AccessArticle

Optimization of Food Industry Production Using the Monte Carlo Simulation Method: A Case Study of a Meat Processing Plant

by Mikhail Koroteev, Ekaterina Romanova, Dmitriy Korovin, Vasiliy Shevtsov, Vadim Feklin, Petr Nikitin, Sergey Makrushin and Konstantin V. Bublikov

Informatics 2022, 9(1), 5; https://doi.org/10.3390/informatics9010005 - 18 Jan 2022

Cited by 6 | Viewed by 4273

Abstract

The problem evaluated in this study is related to the optimization of a budget of an industrial enterprise using simulation methods of the production process. Our goal is to offer a universal and straightforward methodology for simulating a production budget at any level [...] Read more.

The problem evaluated in this study is related to the optimization of a budget of an industrial enterprise using simulation methods of the production process. Our goal is to offer a universal and straightforward methodology for simulating a production budget at any level of complexity by presenting it in a specific form. The calculation of such production schemes, in most enterprises, is currently done manually, which significantly limits the possibilities for optimization. This article proposes a model based on the Monte Carlo method to automate the budgeting process. The application of this model is described using an example of a typical meat processing enterprise. Approbation of the model showed its high applicability and the ability to transform the process of making management decisions and the potential to increase the profits of the enterprise, which is unattainable using other methods. As a result of the study, we present a methodology for modeling industrial production that can significantly speed up the formation and optimization of an enterprise’s budget. In our demonstration case, the profit increased by over 30 percentage points. Full article

(This article belongs to the Special Issue Machine Learning in Industry 4.0: From Predictive Maintenance to Design Support Systems)

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14 pages, 1747 KiB

Open AccessArticle

Automated Intracranial Hematoma Classification in Traumatic Brain Injury (TBI) Patients Using Meta-Heuristic Optimization Techniques

by Vidhya V, U. Raghavendra, Anjan Gudigar, Praneet Kasula, Yashas Chakole, Ajay Hegde, Girish Menon R, Chui Ping Ooi, Edward J. Ciaccio and U. Rajendra Acharya

Informatics 2022, 9(1), 4; https://doi.org/10.3390/informatics9010004 - 10 Jan 2022

Cited by 3 | Viewed by 3513

Abstract

Traumatic Brain Injury (TBI) is a devastating and life-threatening medical condition that can result in long-term physical and mental disabilities and even death. Early and accurate detection of Intracranial Hemorrhage (ICH) in TBI is crucial for analysis and treatment, as the condition can [...] Read more.

Traumatic Brain Injury (TBI) is a devastating and life-threatening medical condition that can result in long-term physical and mental disabilities and even death. Early and accurate detection of Intracranial Hemorrhage (ICH) in TBI is crucial for analysis and treatment, as the condition can deteriorate significantly with time. Hence, a rapid, reliable, and cost-effective computer-aided approach that can initially capture the hematoma features is highly relevant for real-time clinical diagnostics. In this study, the Gray Level Occurrence Matrix (GLCM), the Gray Level Run Length Matrix (GLRLM), and Hu moments are used to generate the texture features. The best set of discriminating features are obtained using various meta-heuristic algorithms, and these optimal features are subjected to different classifiers. The synthetic samples are generated using ADASYN to compensate for the data imbalance. The proposed CAD system attained 95.74% accuracy, 96.93% sensitivity, and 94.67% specificity using statistical and GLRLM features along with KNN classifier. Thus, the developed automated system can enhance the accuracy of hematoma detection, aid clinicians in the fast interpretation of CT images, and streamline triage workflow. Full article

(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)

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12 pages, 784 KiB

Open AccessArticle

The Triadic Relationship of Sense-Making, Analytics, and Institutional Influences

by Imad Bani-Hani, Soumitra Chowdhury and Arianit Kurti

Informatics 2022, 9(1), 3; https://doi.org/10.3390/informatics9010003 - 28 Dec 2021

Cited by 1 | Viewed by 2932

Abstract

The current business environment demands the enablement of organization-wide use of analytics to support a fact-based decision making. Such movement within the organization require employees to take advantage of the self-service business analytics tools to independently fulfil their needs. However, assuming independence in [...] Read more.

The current business environment demands the enablement of organization-wide use of analytics to support a fact-based decision making. Such movement within the organization require employees to take advantage of the self-service business analytics tools to independently fulfil their needs. However, assuming independence in data analytics requires employees to make sense of several elements which collectively contribute to the generation of required insights. Building on sense-making, self-service business analytics, and institutions literature, this paper explores the relationship between sense-making and self-service business analytics and how institutions influence and shape such relationship. By adopting a qualitative perspective and using 22 interviews, we have empirically investigated a model developed through our literature review and provided more understanding of the sense-making concept in a self-service business analytics context. Full article

(This article belongs to the Special Issue Big Data Analytics, AI and Machine Learning in Marketing)

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14 pages, 2900 KiB

Open AccessArticle

A Smart Integrated Vest for the Canine Companion of the K9 Units

by Georgios Vosinakis, Maria Krommyda, Angelos Stamou, Nikos Mitro, Marios Palazis-Aslanidis, Katerina Voulgary, Spyros Athanasiadis and Angelos Amditis

Informatics 2022, 9(1), 2; https://doi.org/10.3390/informatics9010002 - 21 Dec 2021

Cited by 1 | Viewed by 2914

Abstract

Search and rescue operations can range from small, confined spaces, such as collapsed buildings, to large area searches during missing person operations. K9 units are tasked with intervening in such emergencies and assist in the optimal way to ensure a successful outcome for [...] Read more.

Search and rescue operations can range from small, confined spaces, such as collapsed buildings, to large area searches during missing person operations. K9 units are tasked with intervening in such emergencies and assist in the optimal way to ensure a successful outcome for the mission. They are required to operate in unknown situations were the lives of the K9 handler and the canine companion are threatened as they operate with limited situational awareness. Within the context of the INGENIOUS project, we developed a K9 vest for the canine companion of the unit, aiming to increase the unit’s safety while operating in the field, assist the K9 handler in better monitoring the location and the environment of the K9 and increase the information provided to the Command and Control Center during the operation. Full article

(This article belongs to the Section Human-Computer Interaction)

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17 pages, 4732 KiB

Open AccessArticle

Design Thinking: Methodological Strategy for the Creation of a Playful Application for Children with Dyslexia

by Rubén Jerónimo Yedra and María Alejandrina Almeida Aguilar

Informatics 2022, 9(1), 1; https://doi.org/10.3390/informatics9010001 - 21 Dec 2021

Cited by 4 | Viewed by 3467

Abstract

The use of a methodology to address a problem facilitates work in an efficient, effective, and highly productive way. The design thinking methodology (also known as design thinking) is user-centric and oriented towards offering solutions by breaking down a problem into small parts [...] Read more.

The use of a methodology to address a problem facilitates work in an efficient, effective, and highly productive way. The design thinking methodology (also known as design thinking) is user-centric and oriented towards offering solutions by breaking down a problem into small parts to analyze it, to explore it, to test the results, and to create solutions that benefit the end-user. Many children have problems related to learning disorders, such as dyslexia, which occur due to the way that their brain incorporates and processes information. This can lead to them showing difficulty in some learning areas, even when their intelligence or motivation does not appear to be affected. In this research, through a mixed approach, a playful application is developed using new information and communication technologies (ICT), following a design thinking methodology, with the aim of supporting the learning of children with dyslexia through content designed with respect to their needs in order to help improve their academic performance. Data collection was carried out through observation, an interview, and record reviews. Analysis of the didactic materials allowed for the observation that content designed for the specific needs of children can work as a reinforcement for incorporating the information in an entertaining, dynamic, and friendly way, ultimately contributing to improved academic performance. Full article

(This article belongs to the Section Social Informatics and Digital Humanities)

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