Information

15 pages, 1057 KiB

Open AccessArticle

Rebuilding Stakeholder Confidence in Health-Relevant Big Data Applications: A Social Representations Perspective

by Anthony M. Maina and Upasana G. Singh

Information 2022, 13(9), 441; https://doi.org/10.3390/info13090441 - 19 Sep 2022

Cited by 2 | Viewed by 2772

Big data applications are at the epicentre of recent breakthroughs in digital health. However, controversies over privacy, security, ethics, accountability, and data governance have tarnished stakeholder trust, leaving health-relevant big data projects under threat, delayed, or abandoned. Taking the notion of big data [...] Read more.

Big data applications are at the epicentre of recent breakthroughs in digital health. However, controversies over privacy, security, ethics, accountability, and data governance have tarnished stakeholder trust, leaving health-relevant big data projects under threat, delayed, or abandoned. Taking the notion of big data as social construction, this work explores the social representations of the big data concept from the perspective of stakeholders in Kenya’s digital health environment. Through analysing the similarities and differences in the way health professionals and information technology (IT) practitioners comprehend the idea of big data, we draw strategic implications for restoring confidence in big data initiatives. Respondents associated big data with a multiplicity of concepts and were conflicted in how they represented big data’s benefits and challenges. On this point, we argue that peculiarities and nuances in how diverse players view big data contribute to the erosion of trust and the need to revamp stakeholder engagement practices. Specifically, decision makers should complement generalised informational campaigns with targeted, differentiated messages designed to address data responsibility, access, control, security, or other issues relevant to a specialised but influential community. Full article

(This article belongs to the Special Issue Trends in Electronics and Health Informatics)

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11 pages, 1472 KiB

Open AccessReview

An Information Ethics Framework Based on ICT Platforms

by Jeonghye Han

Information 2022, 13(9), 440; https://doi.org/10.3390/info13090440 - 18 Sep 2022

Cited by 11 | Viewed by 5436

Abstract

With continuing developments in artificial intelligence (AI) and robot technology, ethical issues related to digital humans, AI avatars, intelligent process automation, robots, cyborgs, and autonomous vehicles are emerging, and the need for cultural and social sustainability through AI ethics is increasing. Moreover, as [...] Read more.

With continuing developments in artificial intelligence (AI) and robot technology, ethical issues related to digital humans, AI avatars, intelligent process automation, robots, cyborgs, and autonomous vehicles are emerging, and the need for cultural and social sustainability through AI ethics is increasing. Moreover, as the use of video conferencing and metaverse platforms has increased due to COVID-19, ethics concepts and boundaries related to information and communications technology, cyber etiquette, AI ethics, and robot ethics have become more ambiguous. Because the definitions of ethics domains may be confusing due to the various types of computing platforms available, this paper attempts to classify these ethics domains according to three main platforms: computing devices, intermediary platforms, and physical computing devices. This classification provides a conceptual ethics framework that encompasses computer ethics, information ethics, cyber ethics, robot ethics, and AI ethics. Several examples are provided to clarify the boundaries between the various ethics and platforms. The results of this study can be the educational basis for the sustainability of society on ethical issues according to the development of technology. Full article

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31 pages, 4274 KiB

Open AccessArticle

Towards an Accessible Platform for Multimodal Extended Reality Smart Environments

by Emanuela Bran, Gheorghe Nadoleanu and Dorin-Mircea Popovici

Information 2022, 13(9), 439; https://doi.org/10.3390/info13090439 - 18 Sep 2022

Cited by 1 | Viewed by 2507

Abstract

This article presents the DEMOS prototype platform for creating and exploring multimodal extended-reality smart environments. Modular distributed event-driven applications are created with the help of visual codeless design tools for configuring and linking processing nodes in an oriented dataflow graph. We tested the [...] Read more.

This article presents the DEMOS prototype platform for creating and exploring multimodal extended-reality smart environments. Modular distributed event-driven applications are created with the help of visual codeless design tools for configuring and linking processing nodes in an oriented dataflow graph. We tested the conceptual logical templates by building two applications that tackle driver arousal state for safety and enhanced museum experiences for cultural purposes, and later by evaluating programmer and nonprogrammer students’ ability to use the design logic. The applications involve formula-based and decision-based processing of data coming from smart sensors, web services, and libraries. Interaction patterns within the distributed event-driven applications use elements of mixed reality and the Internet of Things, creating an intelligent environment based on near-field communication-triggering points. We discuss the platform as a solution to bridging the digital divide, analyzing novel technologies that support the development of a sustainable digital ecosystem. Full article

(This article belongs to the Collection Augmented Reality Technologies, Systems and Applications)

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12 pages, 876 KiB

Open AccessArticle

Quantum-Inspired Evolutionary Algorithm for Optimal Service-Matching Task Assignment

by Joan Vendrell and Solmaz Kia

Information 2022, 13(9), 438; https://doi.org/10.3390/info13090438 - 17 Sep 2022

Viewed by 1767

Abstract

This paper proposes a quantum-inspired evolutionary algorithm (QiEA) to solve an optimal service-matching task-assignment problem. Our proposed algorithm comes with the advantage of generating always feasible population individuals and, thus, eliminating the necessity for a repair step. That is, with respect to other [...] Read more.

This paper proposes a quantum-inspired evolutionary algorithm (QiEA) to solve an optimal service-matching task-assignment problem. Our proposed algorithm comes with the advantage of generating always feasible population individuals and, thus, eliminating the necessity for a repair step. That is, with respect to other quantum-inspired evolutionary algorithms, our proposed QiEA algorithm presents a new way of collapsing the quantum state that integrates the problem constraints in order to avoid later adjusting operations of the system to make it feasible. This results in lower computations and also faster convergence. We compare our proposed QiEA algorithm with three commonly used benchmark methods: the greedy algorithm, Hungarian method and Simplex, in five different case studies. The results show that the quantum approach presents better scalability and interesting properties that can be used in a wider class of assignment problems where the matching is not perfect. Full article

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17 pages, 4374 KiB

Open AccessArticle

Design Factors of Shared Situation Awareness Interface in Human–Machine Co-Driving

by Fang You, Xu Yan, Jun Zhang and Wei Cui

Information 2022, 13(9), 437; https://doi.org/10.3390/info13090437 - 16 Sep 2022

Cited by 5 | Viewed by 2999

Abstract

Automated vehicles can perceive their environment and control themselves, but how to effectively transfer the information perceived by the vehicles to human drivers through interfaces, or share the awareness of the situation, is a problem to be solved in human–machine co-driving. The four [...] Read more.

Automated vehicles can perceive their environment and control themselves, but how to effectively transfer the information perceived by the vehicles to human drivers through interfaces, or share the awareness of the situation, is a problem to be solved in human–machine co-driving. The four elements of the shared situation awareness (SSA) interface, namely human–machine state, context, current task status, and plan, were analyzed and proposed through an abstraction hierarchy design method to guide the output of the corresponding interface design elements. The four elements were introduced to visualize the interface elements and design the interface prototype in the scenario of “a vehicle overtaking with a dangerous intention from the left rear”, and the design schemes were experimentally evaluated. The results showed that the design with the four elements of an SSA interface could effectively improve the usability of the human–machine interface, increase the levels of human drivers’ situational awareness and prediction of dangerous intentions, and boost trust in the automatic systems, thereby providing ideas for the design of human–machine collaborative interfaces that enhance shared situational awareness in similar scenarios. Full article

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14 pages, 1639 KiB

Open AccessArticle

Explainable Stacking-Based Model for Predicting Hospital Readmission for Diabetic Patients

by Haohui Lu and Shahadat Uddin

Information 2022, 13(9), 436; https://doi.org/10.3390/info13090436 - 15 Sep 2022

Cited by 9 | Viewed by 4070

Abstract

Artificial intelligence is changing the practice of healthcare. While it is essential to employ such solutions, making them transparent to medical experts is more critical. Most of the previous work presented disease prediction models, but did not explain them. Many healthcare stakeholders do [...] Read more.

Artificial intelligence is changing the practice of healthcare. While it is essential to employ such solutions, making them transparent to medical experts is more critical. Most of the previous work presented disease prediction models, but did not explain them. Many healthcare stakeholders do not have a solid foundation in these models. Treating these models as ‘black box’ diminishes confidence in their predictions. The development of explainable artificial intelligence (XAI) methods has enabled us to change the models into a ‘white box’. XAI allows human users to comprehend the results from machine learning algorithms by making them easy to interpret. For instance, the expenditures of healthcare services associated with unplanned readmissions are enormous. This study proposed a stacking-based model to predict 30-day hospital readmission for diabetic patients. We employed Random Under-Sampling to solve the imbalanced class issue, then utilised SelectFromModel for feature selection and constructed a stacking model with base and meta learners. Compared with the different machine learning models, performance analysis showed that our model can better predict readmission than other existing models. This proposed model is also explainable and interpretable. Based on permutation feature importance, the strong predictors were the number of inpatients, the primary diagnosis, discharge to home with home service, and the number of emergencies. The local interpretable model-agnostic explanations method was also employed to demonstrate explainability at the individual level. The findings for the readmission of diabetic patients could be helpful in medical practice and provide valuable recommendations to stakeholders for minimising readmission and reducing public healthcare costs. Full article

(This article belongs to the Special Issue Predictive Analytics and Data Science)

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16 pages, 742 KiB

Open AccessArticle

Shedding Light on the Dark Web: Authorship Attribution in Radical Forums

by Leonardo Ranaldi, Federico Ranaldi, Francesca Fallucchi and Fabio Massimo Zanzotto

Information 2022, 13(9), 435; https://doi.org/10.3390/info13090435 - 14 Sep 2022

Cited by 8 | Viewed by 3186

Abstract

Online users tend to hide their real identities by adopting different names on the Internet. On Facebook or LinkedIn, for example, people usually appear with their real names. On other standard websites, such as forums, people often use nicknames to protect their real [...] Read more.

Online users tend to hide their real identities by adopting different names on the Internet. On Facebook or LinkedIn, for example, people usually appear with their real names. On other standard websites, such as forums, people often use nicknames to protect their real identities. Aliases are used when users are trying to protect their anonymity. This can be a challenge to law enforcement trying to identify users who often change nicknames. In unmonitored contexts, such as the dark web, users expect strong identity protection. Thus, without censorship, these users may create parallel social networks where they can engage in potentially malicious activities that could pose security threats. In this paper, we propose a solution to the need to recognize people who anonymize themselves behind nicknames—the authorship attribution (AA) task—in the challenging context of the dark web: specifically, an English-language Islamic forum dedicated to discussions of issues related to the Islamic world and Islam, in which members of radical Islamic groups are present. We provide extensive analysis by testing models based on transformers, styles, and syntactic features. Downstream of the experiments, we show how models that analyze syntax and style perform better than pre-trained universal language models. Full article

(This article belongs to the Special Issue Novel Methods and Applications in Natural Language Processing)

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19 pages, 9969 KiB

Open AccessArticle

Analysis of the Correlation between Mass-Media Publication Activity and COVID-19 Epidemiological Situation in Early 2022

by Kirill Yakunin, Ravil I. Mukhamediev, Marina Yelis, Yan Kuchin, Adilkhan Symagulov, Vitaly Levashenko, Elena Zaitseva, Margulan Aubakirov, Nadiya Yunicheva, Elena Muhamedijeva, Viktors Gopejenko and Yelena Popova

Information 2022, 13(9), 434; https://doi.org/10.3390/info13090434 - 14 Sep 2022

Cited by 1 | Viewed by 2487

Abstract

The paper presents the results of a correlation analysis between the information trends in the electronic media of Kazakhstan and indicators of the epidemiological situation of COVID-19 according to the World Health Organization (WHO). The developed method is based on topic modeling and [...] Read more.

The paper presents the results of a correlation analysis between the information trends in the electronic media of Kazakhstan and indicators of the epidemiological situation of COVID-19 according to the World Health Organization (WHO). The developed method is based on topic modeling and some other methods of processing natural language texts. The method allows for calculating the correlations between media topics, moods, the results of full-text search queries, and objective WHO data. The analysis of the results shows how the attitudes of society towards the problems of COVID-19 changed from 2021–2022. Firstly, the results reflect a steady trend of decreasing interest of electronic media in the topic of the pandemic, although to an unequal extent for different thematic groups. Secondly, there has been a tendency to shift the focus of attention to more pragmatic issues, such as remote learning problems, remote work, the impact of quarantine restrictions on the economy, etc. Full article

(This article belongs to the Special Issue Novel Methods and Applications in Natural Language Processing)

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11 pages, 1071 KiB

Open AccessArticle

Digital Work and Urban Delivery: Profile, Activity and Mobility Practices of On-Demand Food Delivery Couriers in Paris (France)

by Anne Aguilera, Laetitia Dablanc and Alain Rallet

Information 2022, 13(9), 433; https://doi.org/10.3390/info13090433 - 13 Sep 2022

Cited by 5 | Viewed by 3070

Abstract

Platform-based on-demand delivery services are rapidly developing in urban areas, especially in the food sector, raising new issues for urban planners, especially in the field of transport. Based on a survey of over 100 couriers conducted in 2018 in the municipality of Paris [...] Read more.

Platform-based on-demand delivery services are rapidly developing in urban areas, especially in the food sector, raising new issues for urban planners, especially in the field of transport. Based on a survey of over 100 couriers conducted in 2018 in the municipality of Paris (France), this work aims at analyzing the profile, delivery activity and mobility practices of the couriers working for these platforms. The main objective is to show how mobility practices are shaped by the characteristics of digital work in the urban delivery sector, and to highlight new challenges for urban authorities and research. Compared to other studies, our work is based on quantitative data and distinguishes three categories of couriers, depending on whether they have another activity: students, people with another paid job, and people with no other paid or non-paid activity. Findings show that these three categories have different characteristics, regarding age, education, residential location, the intensity of delivery activity and the characteristics of mobility practices, especially regarding the transport modes used. The article ends with the discussion of a number of new challenges for both urban authorities and researchers regarding the sustainability of these new forms of digital work in urban delivery. Full article

(This article belongs to the Special Issue Digital Work—Information Technology and Commute Choice)

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1 pages, 173 KiB

Open AccessCorrection

Correction: Hayat et al. Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches. Information 2022, 13, 275

by Ahatsham Hayat, Fernando Morgado-Dias, Bikram Pratim Bhuyan and Ravi Tomar

Information 2022, 13(9), 432; https://doi.org/10.3390/info13090432 - 13 Sep 2022

Viewed by 1238

Abstract

In the original publication [...] Full article

19 pages, 328 KiB

Open AccessArticle

Strategic Assessment of Cyber Security Contenders to the Brazilian Agribusiness in the Beef Sector

by Virgínia de Melo Dantas Trinks, Robson de Oliveira Albuquerque, Rafael Rabelo Nunes and Gibran Ayupe Mota

Information 2022, 13(9), 431; https://doi.org/10.3390/info13090431 - 13 Sep 2022

Cited by 2 | Viewed by 3520

Abstract

The current international commercial structure places Brazilian Agribusiness in constant conflict to protect its interests before other nations in the global market. Technological innovations are used in all stages from the simplest production tasks, up to the design of negotiation tactics at high-level [...] Read more.

The current international commercial structure places Brazilian Agribusiness in constant conflict to protect its interests before other nations in the global market. Technological innovations are used in all stages from the simplest production tasks, up to the design of negotiation tactics at high-level affairs. This paper has the objective of finding Brazilian contenders in the beef market with cyber capabilities and commercial interest to act in favor of their interests. To construct such a list, a review of the literature on Threat and Cyber Threat Intelligence is presented, followed by a background presentation of how embedded technology is in nowadays agriculture and supply chains in general, and the real necessity for those sectors to be seen as critical infrastructure by governments in general. Also as background information recent cyber attack cases and attacker countries are shown. A Step-by-Step multidisciplinary method is presented that involves the extent of international trade, the interest on specific markets, and the intersection of country cyber capacity index. After applying the method and criteria generated a list of five contender countries. The method may be replicated and/or applied, considering adequate data source assessment and following specifics of each sector. Full article

(This article belongs to the Special Issue Novel Approaches for Information Security in Complex Cyber-Physical Systems)

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18 pages, 790 KiB

Open AccessReview

Ultra-Reliable Low-Latency Communications: Unmanned Aerial Vehicles Assisted Systems

by Mohamed Osama, Abdelhamied A. Ateya, Shaimaa Ahmed Elsaid and Ammar Muthanna

Information 2022, 13(9), 430; https://doi.org/10.3390/info13090430 - 12 Sep 2022

Cited by 12 | Viewed by 5192

Abstract

Ultra-reliable low-latency communication (uRLLC) is a group of fifth-generation and sixth-generation (5G/6G) cellular applications with special requirements regarding latency, reliability, and availability. Most of the announced 5G/6G applications are uRLLC that require an end-to-end latency of milliseconds and ultra-high reliability of communicated data. [...] Read more.

Ultra-reliable low-latency communication (uRLLC) is a group of fifth-generation and sixth-generation (5G/6G) cellular applications with special requirements regarding latency, reliability, and availability. Most of the announced 5G/6G applications are uRLLC that require an end-to-end latency of milliseconds and ultra-high reliability of communicated data. Such systems face many challenges since traditional networks cannot meet such requirements. Thus, novel network structures and technologies have been introduced to enable such systems. Since uRLLC is a promising paradigm that covers many applications, this work considers reviewing the current state of the art of the uRLLC. This includes the main applications, specifications, and main requirements of ultra-reliable low-latency (uRLL) applications. The design challenges of uRLLC systems are discussed, and promising solutions are introduced. The virtual and augmented realities (VR/AR) are considered the main use case of uRLLC, and the current proposals for VR and AR are discussed. Moreover, unmanned aerial vehicles (UAVs) are introduced as enablers of uRLLC. The current research directions and the existing proposals are discussed. Full article

(This article belongs to the Special Issue Advances in Wireless Communications Systems)

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17 pages, 916 KiB

Open AccessArticle

Extending the Technology Acceptance Model 3 to Incorporate the Phenomenon of Warm-Glow

by Antonios Saravanos, Stavros Zervoudakis and Dongnanzi Zheng

Information 2022, 13(9), 429; https://doi.org/10.3390/info13090429 - 12 Sep 2022

Cited by 5 | Viewed by 7760

Abstract

In this paper, we extend the third evolution of the Technology Acceptance Model (TAM3) to incorporate warm-glow with the aim of understanding the role this phenomenon plays on user adoption decisions. Warm-glow is the feeling of satisfaction or pleasure (or both) that is [...] Read more.

In this paper, we extend the third evolution of the Technology Acceptance Model (TAM3) to incorporate warm-glow with the aim of understanding the role this phenomenon plays on user adoption decisions. Warm-glow is the feeling of satisfaction or pleasure (or both) that is experienced by individuals after they do something “good” for their fellow human. Two constructs—perceived extrinsic warm-glow (PEWG) and perceived intrinsic warm-glow (PIWG)—were incorporated into the TAM3 model to measure the two dimensions of user-experienced warm-glow, forming what we refer to as the TAM3 + WG model. An experimental approach was taken to evaluate the suitability of the proposed model (i.e., TAM3 + WG). A vignette was created to present users with a hypothetical technology designed to evoke warm-glow in participants. Our TAM3 + WG model was found to be superior in terms of fit to the TAM3 model. Furthermore, the PEWG and PIWG constructs were confirmed to be unique within the original TAM3 model. The findings indicate that the factors that have the greatest influence on consumer decisions are (in decreasing order) perceived usefulness (PU), PIWG, subjective norm (SN), and PEWG. Additionally, a higher PEWG resulted in the technology being perceived as more useful. In other words, both extrinsic and intrinsic warm-glow play a prominent role in user decisions as to whether or not to adopt a particular technology. Full article

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13 pages, 1088 KiB

Open AccessArticle

On Exploiting Rating Prediction Accuracy Features in Dense Collaborative Filtering Datasets

by Dimitris Spiliotopoulos, Dionisis Margaris and Costas Vassilakis

Information 2022, 13(9), 428; https://doi.org/10.3390/info13090428 - 11 Sep 2022

Cited by 5 | Viewed by 1910

Abstract

One of the typical goals of collaborative filtering algorithms is to produce rating predictions with values very close to what real users would give to an item. Afterward, the items having the largest rating prediction values will be recommended to the users by [...] Read more.

One of the typical goals of collaborative filtering algorithms is to produce rating predictions with values very close to what real users would give to an item. Afterward, the items having the largest rating prediction values will be recommended to the users by the recommender system. Collaborative filtering algorithms can be applied to both sparse and dense datasets, and each of these dataset categories involves different kinds of risks. As far as the dense collaborative filtering datasets are concerned, where the rating prediction coverage is, most of the time, very high, we usually face large rating prediction times, issues concerning the selection of a user’s near neighbours, etc. Although collaborative filtering algorithms usually achieve better results when applied to dense datasets, there is still room for improvement, since in many cases, the rating prediction error is relatively high, which leads to unsuccessful recommendations and hence to recommender system unreliability. In this work, we explore rating prediction accuracy features, although in a broader context, in dense collaborative filtering datasets. We conduct an extensive evaluation, using dense datasets, widely used in collaborative filtering research, in order to find the associations between these features and the rating prediction accuracy. Full article

(This article belongs to the Special Issue Recommendation Algorithms and Web Mining)

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25 pages, 3129 KiB

Open AccessArticle

Edge and Fog Computing Business Value Streams through IoT Solutions: A Literature Review for Strategic Implementation

by Nikolaos-Alexandros Perifanis and Fotis Kitsios

Information 2022, 13(9), 427; https://doi.org/10.3390/info13090427 - 11 Sep 2022

Cited by 4 | Viewed by 4710

Abstract

Edge–fog computing and IoT have the ability to revolutionize businesses across all sectors and functions, from customer engagement to manufacturing, which is what makes them so fascinating and emerging. On the basis of research methodology by Webster and Watson (2020), 124 peer-reviewed articles [...] Read more.

Edge–fog computing and IoT have the ability to revolutionize businesses across all sectors and functions, from customer engagement to manufacturing, which is what makes them so fascinating and emerging. On the basis of research methodology by Webster and Watson (2020), 124 peer-reviewed articles were discussed. According to the literature, these technologies lead to reduced latency, costs, bandwidth, and disruption, but at the same time, they improved response time, compliance, security and greater autonomy. The results of this review revealed the open issues and topics which call for further research/examination in order for edge–fog computing to unveil new business value streams along with IoT capabilities for the organizations. Only by adopting and implementing precisely these revolutionary will new solutions organizations succeed in the digital transformation of the modern era. Despite the fact that they are cutting-edge solutions to business operations and knowledge creation, there are still practical implementation issues to be dealt with and a lack of experience in the strategic integration of the variable architectures, which hinder efforts to generate business value. Full article

(This article belongs to the Special Issue Wireless Edge Computing: Enabling Technologies for the Next Generation of Cloud Computing)

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22 pages, 2952 KiB

Open AccessArticle

Fake News Spreaders Detection: Sometimes Attention Is Not All You Need

by Marco Siino, Elisa Di Nuovo, Ilenia Tinnirello and Marco La Cascia

Information 2022, 13(9), 426; https://doi.org/10.3390/info13090426 - 9 Sep 2022

Cited by 25 | Viewed by 4777

Abstract

Guided by a corpus linguistics approach, in this article we present a comparative evaluation of State-of-the-Art (SotA) models, with a special focus on Transformers, to address the task of Fake News Spreaders (i.e., users that share Fake News) detection. First, we explore the [...] Read more.

Guided by a corpus linguistics approach, in this article we present a comparative evaluation of State-of-the-Art (SotA) models, with a special focus on Transformers, to address the task of Fake News Spreaders (i.e., users that share Fake News) detection. First, we explore the reference multilingual dataset for the considered task, exploiting corpus linguistics techniques, such as chi-square test, keywords and Word Sketch. Second, we perform experiments on several models for Natural Language Processing. Third, we perform a comparative evaluation using the most recent Transformer-based models (RoBERTa, DistilBERT, BERT, XLNet, ELECTRA, Longformer) and other deep and non-deep SotA models (CNN, MultiCNN, Bayes, SVM). The CNN tested outperforms all the models tested and, to the best of our knowledge, any existing approach on the same dataset. Fourth, to better understand this result, we conduct a post-hoc analysis as an attempt to investigate the behaviour of the presented best performing black-box model. This study highlights the importance of choosing a suitable classifier given the specific task. To make an educated decision, we propose the use of corpus linguistics techniques. Our results suggest that large pre-trained deep models like Transformers are not necessarily the first choice when addressing a text classification task as the one presented in this article. All the code developed to run our tests is publicly available on GitHub. Full article

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13 pages, 1839 KiB

Open AccessArticle

Usability Evaluation of an Adaptive Serious Game Prototype Based on Affective Feedback

by Lampros Karavidas, Hippokratis Apostolidis and Thrasyvoulos Tsiatsos

Information 2022, 13(9), 425; https://doi.org/10.3390/info13090425 - 8 Sep 2022

Cited by 6 | Viewed by 2355

Abstract

Difficulty in video games is an essential factor for a game to be considered engaging and is directly linked to losing in a game. However, for the user to not feel bored or frustrated, it is necessary for the difficulty of the game [...] Read more.

Difficulty in video games is an essential factor for a game to be considered engaging and is directly linked to losing in a game. However, for the user to not feel bored or frustrated, it is necessary for the difficulty of the game to be balanced and ideally tailored to the user. This paper presents the design and development of a serious game that adjusts its difficulty based on the user’s bio signals, so that it is demanding enough to match his/her skills, in order to enter the flow state. The serious game is accompanied by a server that uses machine learning algorithms to analyze the user’s bio signals and classify them into different affective states. These states are later used to adjust the difficulty of the serious game in real-time, without interfering with the user’s game experience. Finally, a heuristic evaluation was conducted in order to measure its usability and highlight the good practices and to draw attention to some elements of the game that should be changed in a future version. Full article

(This article belongs to the Special Issue Artificial Intelligence and Games Science in Education)

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17 pages, 4731 KiB

Open AccessArticle

A Flexible Data Evaluation System for Improving the Quality and Efficiency of Laboratory Analysis and Testing

by Yonghui Tu, Haoye Tang, Hua Gong and Wenyou Hu

Information 2022, 13(9), 424; https://doi.org/10.3390/info13090424 - 8 Sep 2022

Cited by 1 | Viewed by 2200

Abstract

In a chemical analysis laboratory, sample detection via most analytical devices obtains raw data and processes it to validate data reports, including raw data filtering, editing, effectiveness evaluation, error correction, etc. This process is usually carried out manually by analysts. When the sample [...] Read more.

In a chemical analysis laboratory, sample detection via most analytical devices obtains raw data and processes it to validate data reports, including raw data filtering, editing, effectiveness evaluation, error correction, etc. This process is usually carried out manually by analysts. When the sample detection volume is large, the data processing involved becomes time-consuming and laborious, and manual errors may be introduced. In addition, analytical laboratories typically use a variety of analytical devices with different measurement principles, leading to the use of various heterogeneous control software systems from different vendors with different export data formats. Different formats introduce difficulties to laboratory automation. This paper proposes a modular data evaluation system that uses a global unified management and maintenance mode that can automatically filter data, evaluate quality, generate valid reports, and distribute reports. This modular software design concept allows the proposed system to be applied to different analytical devices; its integration into existing laboratory information management systems (LIMS) could maximise automation and improve the analysis and testing quality and efficiency in a chemical analysis laboratory, while meeting the analysis and testing requirements. Full article

(This article belongs to the Special Issue Information for Business and Management–Software Development for Data Processing and Management)

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27 pages, 2313 KiB

Open AccessArticle

RAMi: A New Real-Time Internet of Medical Things Architecture for Elderly Patient Monitoring

by Olivier Debauche, Jean Bertin Nkamla Penka, Saïd Mahmoudi, Xavier Lessage, Moad Hani, Pierre Manneback, Uriel Kanku Lufuluabu, Nicolas Bert, Dounia Messaoudi and Adriano Guttadauria

Information 2022, 13(9), 423; https://doi.org/10.3390/info13090423 - 7 Sep 2022

Cited by 23 | Viewed by 5040

Abstract

The aging of the world’s population, the willingness of elderly to remain independent, and the recent COVID-19 pandemic have demonstrated the urgent need for home-based diagnostic and patient monitoring systems to reduce the financial and organizational burdens that impact healthcare organizations and professionals. [...] Read more.

The aging of the world’s population, the willingness of elderly to remain independent, and the recent COVID-19 pandemic have demonstrated the urgent need for home-based diagnostic and patient monitoring systems to reduce the financial and organizational burdens that impact healthcare organizations and professionals. The Internet of Medical Things (IoMT), i.e., all medical devices and applications that connect to health information systems through online computer networks. The IoMT is one of the domains of IoT where real-time processing of data and reliability are crucial. In this paper, we propose RAMi, which is a Real-Time Architecture for the Monitoring of elderly patients thanks to the Internet of Medical Things. This new architecture includes a Things layer where data are retrieved from sensors or smartphone, a Fog layer built on a smart gateway, Mobile Edge Computing (MEC), a cloud component, blockchain, and Artificial Intelligence (AI) to address the specific problems of IoMT. Data are processed at Fog level, MEC or cloud in function of the workload, resource requirements, and the level of confidentiality. A local blockchain allows workload orchestration between Fog, MEC, and Cloud while a global blockchain secures exchanges and data sharing by means of smart contracts. Our architecture allows to follow elderly persons and patients during and after their hospitalization. In addition, our architecture allows the use of federated learning to train AI algorithms while respecting privacy and data confidentiality. AI is also used to detect patterns of intrusion. Full article

(This article belongs to the Special Issue Wireless Edge Computing: Enabling Technologies for the Next Generation of Cloud Computing)

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15 pages, 8355 KiB

Open AccessArticle

Decipherment Challenges Due to Tamga and Letter Mix-Ups in an Old Hungarian Runic Inscription from the Altai Mountains

by Peter Z. Revesz

Information 2022, 13(9), 422; https://doi.org/10.3390/info13090422 - 7 Sep 2022

Cited by 3 | Viewed by 2534

Abstract

An Old Hungarian Runic inscription from the Altai Mountains with 40 signs has posed some special challenges for decipherment due to several letter mix-ups and the use of a tamga sign, which is the first reported use of a tamga within this type [...] Read more.

An Old Hungarian Runic inscription from the Altai Mountains with 40 signs has posed some special challenges for decipherment due to several letter mix-ups and the use of a tamga sign, which is the first reported use of a tamga within this type of script. This paper gives a complete and correct translation and draws some lessons that can be learned about decipherment. It introduces sign similarity matrices as a method of detecting accidental misspellings and shows that sign similarity matrices can be efficiently computed. It also explains the importance of simultaneously achieving the three criteria for a valid decipherment: correct signs, syntax, and semantics. Full article

(This article belongs to the Special Issue Computational Linguistics and Natural Language Processing)

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17 pages, 2208 KiB

Open AccessArticle

A Time-Varying Incentive Optimization for Interactive Demand Response Based on Two-Step Clustering

by Fei Li, Bo Gao, Lun Shi, Hongtao Shen, Peng Tao, Hongxi Wang, Yehua Mao and Yiyi Zhao

Information 2022, 13(9), 421; https://doi.org/10.3390/info13090421 - 7 Sep 2022

Cited by 2 | Viewed by 1639

Abstract

With the increasing marketization of electricity, residential users are gradually participating in various businesses of power utility companies, and there are more and more interactive adjustments between load, source, and grid. However, the participation of large-scale users has also brought challenges to the [...] Read more.

With the increasing marketization of electricity, residential users are gradually participating in various businesses of power utility companies, and there are more and more interactive adjustments between load, source, and grid. However, the participation of large-scale users has also brought challenges to the grid companies in carrying out demand-side dispatching work. The user load response is uneven, and users’ behavioral characteristics are highly differentiated. It is necessary to consider the differences in users’ electricity consumption demand in the design of the peak–valley load time-sharing incentives, and to adopt a more flexible incentive form. In this context, this paper first establishes a comprehensive clustering method integrating k-means and self-organizing networks (SONs) for the two-step clustering and a BP neural network for reverse adjustment and correction. Then, a time-varying incentive optimization for interactive demand response based on two-step clustering is introduced. Furthermore, based on the different clustering results of customers, the peak–valley load time-sharing incentives are formulated. The proposed method is validated through case studies, where the results indicate that our method can effectively improve the users’ load characteristics and reduce the users’ electricity costs compared to the existing methods. Full article

(This article belongs to the Special Issue Cyber–Physical–Social System for Sustainable Energy)

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35 pages, 11692 KiB

Open AccessArticle

Ghost on the Windshield: Employing a Virtual Human Character to Communicate Pedestrian Acknowledgement and Vehicle Intention

by Alexandros Rouchitsas and Håkan Alm

Information 2022, 13(9), 420; https://doi.org/10.3390/info13090420 - 7 Sep 2022

Cited by 9 | Viewed by 3074

Abstract

Pedestrians base their street-crossing decisions on vehicle-centric as well as driver-centric cues. In the future, however, drivers of autonomous vehicles will be preoccupied with non-driving related activities and will thus be unable to provide pedestrians with relevant communicative cues. External human–machine interfaces (eHMIs) [...] Read more.

Pedestrians base their street-crossing decisions on vehicle-centric as well as driver-centric cues. In the future, however, drivers of autonomous vehicles will be preoccupied with non-driving related activities and will thus be unable to provide pedestrians with relevant communicative cues. External human–machine interfaces (eHMIs) hold promise for filling the expected communication gap by providing information about a vehicle’s situational awareness and intention. In this paper, we present an eHMI concept that employs a virtual human character (VHC) to communicate pedestrian acknowledgement and vehicle intention (non-yielding; cruising; yielding). Pedestrian acknowledgement is communicated via gaze direction while vehicle intention is communicated via facial expression. The effectiveness of the proposed anthropomorphic eHMI concept was evaluated in the context of a monitor-based laboratory experiment where the participants performed a crossing intention task (self-paced, two-alternative forced choice) and their accuracy in making appropriate street-crossing decisions was measured. In each trial, they were first presented with a 3D animated sequence of a VHC (male; female) that either looked directly at them or clearly to their right while producing either an emotional (smile; angry expression; surprised expression), a conversational (nod; head shake), or a neutral (neutral expression; cheek puff) facial expression. Then, the participants were asked to imagine they were pedestrians intending to cross a one-way street at a random uncontrolled location when they saw an autonomous vehicle equipped with the eHMI approaching from the right and indicate via mouse click whether they would cross the street in front of the oncoming vehicle or not. An implementation of the proposed concept where non-yielding intention is communicated via the VHC producing either an angry expression, a surprised expression, or a head shake; cruising intention is communicated via the VHC puffing its cheeks; and yielding intention is communicated via the VHC nodding, was shown to be highly effective in ensuring the safety of a single pedestrian or even two co-located pedestrians without compromising traffic flow in either case. The implications for the development of intuitive, culture-transcending eHMIs that can support multiple pedestrians in parallel are discussed. Full article

(This article belongs to the Section Information Applications)

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17 pages, 1739 KiB

Open AccessEditor’s ChoiceArticle

Local Multi-Head Channel Self-Attention for Facial Expression Recognition

by Roberto Pecoraro, Valerio Basile and Viviana Bono

Information 2022, 13(9), 419; https://doi.org/10.3390/info13090419 - 6 Sep 2022

Cited by 45 | Viewed by 3948

Abstract

Since the Transformer architecture was introduced in 2017, there has been many attempts to bring the self-attention paradigm in the field of computer vision. In this paper, we propose LHC: Local multi-Head Channel self-attention, a novel self-attention module that can be [...] Read more.

Since the Transformer architecture was introduced in 2017, there has been many attempts to bring the self-attention paradigm in the field of computer vision. In this paper, we propose LHC: Local multi-Head Channel self-attention, a novel self-attention module that can be easily integrated into virtually every convolutional neural network, and that is specifically designed for computer vision, with a specific focus on facial expression recognition. LHC is based on two main ideas: first, we think that in computer vision, the best way to leverage the self-attention paradigm is the channel-wise application instead of the more well explored spatial attention. Secondly, a local approach has the potential to better overcome the limitations of convolution than global attention, at least in those scenarios where images have a constant general structure, as in facial expression recognition. LHC-Net achieves a new state-of-the-art in the FER2013 dataset, with a significantly lower complexity and impact on the “host” architecture in terms of computational cost when compared with the previous state-of-the-art. Full article

(This article belongs to the Section Artificial Intelligence)

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19 pages, 2999 KiB

Open AccessArticle

A Cognitive Model to Anticipate Variations of Situation Awareness and Attention for the Takeover in Highly Automated Driving

by Marlene Susanne Lisa Scharfe-Scherf, Sebastian Wiese and Nele Russwinkel

Information 2022, 13(9), 418; https://doi.org/10.3390/info13090418 - 6 Sep 2022

Cited by 10 | Viewed by 2634

Abstract

The development of highly automated driving requires dynamic approaches that anticipate the cognitive state of the driver. In this paper, a cognitive model is developed that simulates a spectrum of cognitive processing and the development of situation awareness and attention guidance in different [...] Read more.

The development of highly automated driving requires dynamic approaches that anticipate the cognitive state of the driver. In this paper, a cognitive model is developed that simulates a spectrum of cognitive processing and the development of situation awareness and attention guidance in different takeover situations. In order to adapt cognitive assistance systems according to individuals in different situations, it is necessary to understand and simulate dynamic processes that are performed during a takeover. To validly represent cognitive processing in a dynamic environment, the model covers different strategies of cognitive and visual processes during the takeover. To simulate the visual processing in detail, a new module for the visual attention within different traffic environments is used. The model starts with a non-driving-related task, attends the takeover request, makes an action decision and executes the corresponding action. It is evaluated against empirical data in six different driving scenarios, including three maneuvers. The interaction with different dynamic traffic scenarios that vary in their complexity is additionally represented within the model. Predictions show variances in reaction times. Furthermore, a spectrum of driving behavior in certain situations is represented and how situation awareness is gained during the takeover process. Based on such a cognitive model, an automated system could classify the driver’s takeover readiness, derive the expected takeover quality and adapt the cognitive assistance for takeovers accordingly to increase safety. Full article

(This article belongs to the Special Issue Test and Evaluation Methods for Human-Machine Interfaces of Automated Vehicles II)

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14 pages, 13927 KiB

Open AccessArticle

Location Privacy-Preserving Query Scheme Based on the Moore Curve and Multi-User Cache

by Zhenpeng Liu, Qiannan Liu, Jianhang Wei, Dewei Miao and Jingyi Wang

Information 2022, 13(9), 417; https://doi.org/10.3390/info13090417 - 6 Sep 2022

Viewed by 1772

Abstract

With the rapid development of the Internet of Things, location-based services have emerged in many social and business fields. In obtaining the service, the user needs to transmit the query data to an untrusted location service provider for query and then obtain the [...] Read more.

With the rapid development of the Internet of Things, location-based services have emerged in many social and business fields. In obtaining the service, the user needs to transmit the query data to an untrusted location service provider for query and then obtain the required content. Most existing schemes tend to protect the user’s location privacy information while ignoring the user’s query privacy. This paper proposes a secure and effective query privacy protection scheme. The multi-user cache is used to store historical query results, reduce the number of communications between users and untrusted servers, and introduce trust computing for malicious users in neighbor caches, thereby reducing the possibility of privacy leakage. When the cache cannot meet the demand, the user’s location coordinates are converted using the Moore curve, processed using encryption technology, and sent to the location service provider to prevent malicious entities from accessing the transformed data. Finally, we simulate and evaluate the scheme on real datasets, and the experimental results demonstrate the safety and effectiveness of the scheme. Full article

(This article belongs to the Section Information Security and Privacy)

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11 pages, 1017 KiB

Open AccessArticle

Digitalization and Strategic Changes in Romanian Retail Fuel Networks: A Qualitative Study

by Dan Andrei Panduru and Cezar Scarlat

Information 2022, 13(9), 416; https://doi.org/10.3390/info13090416 - 1 Sep 2022

Cited by 4 | Viewed by 2947

Abstract

The oil and gas industry is among the most affected industries as a result of war in Ukraine, on top of other economic, political, and environmental global turbulences that culminated with the coronavirus pandemic. The purpose of this qualitative, explorative study was to [...] Read more.

The oil and gas industry is among the most affected industries as a result of war in Ukraine, on top of other economic, political, and environmental global turbulences that culminated with the coronavirus pandemic. The purpose of this qualitative, explorative study was to identify strategic changes as well as the role played by newer technologies—digital technologies in particular—in this industry. The focus is on the Romanian oil and gas industry, more specifically on the retail fuel networks of the top companies. In addition to secondary research (literature and company documents), interview-based primary research was conducted. The data were collected during spring of 2022 by conducting interviews with two groups of subjects: the strategists—consisting of top managers from the largest companies active in the oil and gas industry in Romania; and the informed consumers—selected from people working in the oil and gas industry. The interview guides were slightly different depending on the two groups targeted, and the structure of the interview guide was developed according to research questions. Among the findings, we can observe that the fuel retail market and consumer behaviour changed due to a series of factors, such as the global economic crisis, COVID-19, the Russian invasion of Ukraine, and inflation. Those factors forced fuel retail companies, at the global level, to invest in filling station shops, services development, digitalization, and divestment—selling gas station networks in countries with poor integration with refineries. Romanian fuel retail companies are following the global trends and focusing on filling station shops, alternative fuels development, and digitalization. The results are followed by discussions, and several managerial implications are suggested. The study limitations and several further research paths are also identified. Based on the data available, we can conclude that the strategic directions at the level of products and services are aligned, but at the execution level, specialists offer different solutions for customer expectations. Full article

(This article belongs to the Special Issue The ICT Influence on Strategic Thinking)

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15 pages, 1341 KiB

Open AccessArticle

Automatically Generated Visual Profiles of Code Solutions as Feedback for Students

by Jakub Swacha

Information 2022, 13(9), 415; https://doi.org/10.3390/info13090415 - 1 Sep 2022

Viewed by 1846

Abstract

Providing students feedback on their exercise solutions is a crucial element of computer programming education. Such feedback can be generated automatically and can take various forms. This paper introduces and proposes the use of visual profiles of code solutions as a form of [...] Read more.

Providing students feedback on their exercise solutions is a crucial element of computer programming education. Such feedback can be generated automatically and can take various forms. This paper introduces and proposes the use of visual profiles of code solutions as a form of automatically generated feedback to programming students. The visual profiles are based on the frequency of code elements belonging to six distinct classes. The core idea is to visually compare a profile of a student-submitted solution code to the range of profiles of accepted solutions (including both reference solutions provided by instructors and solutions submitted by students who successfully passed the same exercise earlier). The advantages of the proposed approach are demonstrated on a number of examples based on real-world data. Full article

(This article belongs to the Special Issue Artificial Intelligence and Games Science in Education)

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23 pages, 621 KiB

Open AccessArticle

Information Adoption Patterns and Online Knowledge Payment Behavior: The Moderating Role of Product Type

by Mohammad Daradkeh, Amjad Gawanmeh and Wathiq Mansoor

Information 2022, 13(9), 414; https://doi.org/10.3390/info13090414 - 31 Aug 2022

Cited by 10 | Viewed by 4973

Abstract

The development of online knowledge payment platforms in recent years has increased their respective market value by nurturing content resources and improving content ecology. Yet, the underlying factors of knowledge seekers’ payment behaviors and their information adoption mechanisms are poorly understood. Based on [...] Read more.

The development of online knowledge payment platforms in recent years has increased their respective market value by nurturing content resources and improving content ecology. Yet, the underlying factors of knowledge seekers’ payment behaviors and their information adoption mechanisms are poorly understood. Based on the information adoption model, this study develops a research model to examine the relationship between information adoption patterns and knowledge seekers’ payment behavior, and explore the moderating effect of product type on this relationship. To test the research model and hypotheses, we used a multi-analytic approach combining text and regression analysis on a sample of 4366 social Q&A data collected from Quora+ between August 2021 and August 2022. We further classified the product types into utilitarian and hedonic, and compared the differences in influence paths between product types. The results show that the completeness, vividness, and relevance of the product description have a significant positive impact on knowledge payment behavior. The reputation, experience, and integrity of the knowledge provider have a positive impact on knowledge payment behavior. Compared to utilitarian knowledge products, the payment behavior for hedonic products is more related to the reputation and experience of the knowledge provider. This study provides insights into the factors that influence online knowledge payment behavior and practical guidance for the development of online knowledge payment services and platforms. Full article

(This article belongs to the Special Issue Information Sharing and Knowledge Management)

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17 pages, 1183 KiB

Open AccessArticle

Cybersecurity Behavior among Government Employees: The Role of Protection Motivation Theory and Responsibility in Mitigating Cyberattacks

by Noor Suhani Sulaiman, Muhammad Ashraf Fauzi, Suhaidah Hussain and Walton Wider

Information 2022, 13(9), 413; https://doi.org/10.3390/info13090413 - 31 Aug 2022

Cited by 12 | Viewed by 6894

Abstract

This study examines the factors influencing government employees’ cybersecurity behavior in Malaysia. The country is considered the most vulnerable in Southeast Asia. Applying the protection motivation theory, this study addresses the gap by investigating how government employees behave toward corresponding cyberrisks and threats. [...] Read more.

This study examines the factors influencing government employees’ cybersecurity behavior in Malaysia. The country is considered the most vulnerable in Southeast Asia. Applying the protection motivation theory, this study addresses the gap by investigating how government employees behave toward corresponding cyberrisks and threats. Using partial least-squares structural equation modeling (PLS-SEM), 446 respondents participated and were analyzed. The findings suggest that highly motivated employees with high severity, vulnerability, response efficacy, and self-efficacy exercise cybersecurity. Incorporating the users’ perceptions of vulnerability and severity facilitates behavioral change and increases the understanding of cybersecurity behavior’s role in addressing cybersecurity threats—particularly the impact of the threat response in predicting the cybersecurity behavior of government employees. The implications include providing robust information security protection to the government information systems. Full article

(This article belongs to the Section Information Security and Privacy)

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15 pages, 5113 KiB

Open AccessArticle

Virtual CT Myelography: A Patch-Based Machine Learning Model to Improve Intraspinal Soft Tissue Visualization on Unenhanced Dual-Energy Lumbar Spine CT

by Xuan V. Nguyen, Devi D. Nelakurti, Engin Dikici, Sema Candemir, Daniel J. Boulter and Luciano M. Prevedello

Information 2022, 13(9), 412; https://doi.org/10.3390/info13090412 - 31 Aug 2022

Viewed by 2867

Abstract

Background: Distinguishing between the spinal cord and cerebrospinal fluid (CSF) non-invasively on CT is challenging due to their similar mass densities. We hypothesize that patch-based machine learning applied to dual-energy CT can accurately distinguish CSF from neural or other tissues based on [...] Read more.

Background: Distinguishing between the spinal cord and cerebrospinal fluid (CSF) non-invasively on CT is challenging due to their similar mass densities. We hypothesize that patch-based machine learning applied to dual-energy CT can accurately distinguish CSF from neural or other tissues based on the center voxel and neighboring voxels. Methods: 88 regions of interest (ROIs) from 12 patients’ dual-energy (100 and 140 kVp) lumbar spine CT exams were manually labeled by a neuroradiologist as one of 4 major tissue types (water, fat, bone, and nonspecific soft tissue). Four-class classifier convolutional neural networks were trained, validated, and tested on thousands of nonoverlapping patches extracted from 82 ROIs among 11 CT exams, with each patch representing pixel values (at low and high energies) of small, rectangular, 3D CT volumes. Different patch sizes were evaluated, ranging from 3 × 3 × 3 × 2 to 7 × 7 × 7 × 2. A final ensemble model incorporating all patch sizes was tested on patches extracted from six ROIs in a holdout patient. Results: Individual models showed overall test accuracies ranging from 99.8% for 3 × 3 × 3 × 2 patches (N = 19,423) to 98.1% for 7 × 7 × 7 × 2 patches (N = 1298). The final ensemble model showed 99.4% test classification accuracy, with sensitivities and specificities of 90% and 99.6%, respectively, for the water class and 98.6% and 100% for the soft tissue class. Conclusions: Convolutional neural networks utilizing local low-level features on dual-energy spine CT can yield accurate tissue classification and enhance the visualization of intraspinal neural tissue. Full article

(This article belongs to the Special Issue Stitching, Alignment and Segmentation Applications in Biomedical Images)

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