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Information, Volume 13, Issue 4 (April 2022) – 48 articles

Cover Story (view full-size image): Non-alcoholic fatty pancreas disease is a common condition that can lead to the development of critical pathogens such as type 2 diabetes mellitus, atherosclerosis, acute pancreatitis, and pancreatic cancer. This work aims to solve the issue of steatosis prevalence quantification in pancreatic biopsy specimens with a methodology that employs image segmentation and ensemble convolutional neural network classification techniques. The ensemble decision model is based on a voting system for the separation of fat cells from histological artifacts in order to reduce the diagnostic error. The proposed method presents an 0.08% mean fat quantification error and 83.3% mean dice fat segmentation similarity compared to the semi-quantitative estimates of specialist physicians. View this paper
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17 pages, 15504 KiB  
Review
Sign Language Avatars: A Question of Representation
by Rosalee Wolfe, John C. McDonald, Thomas Hanke, Sarah Ebling, Davy Van Landuyt, Frankie Picron, Verena Krausneker, Eleni Efthimiou, Evita Fotinea and Annelies Braffort
Information 2022, 13(4), 206; https://doi.org/10.3390/info13040206 - 18 Apr 2022
Cited by 13 | Viewed by 6670
Abstract
Given the achievements in automatically translating text from one language to another, one would expect to see similar advancements in translating between signed and spoken languages. However, progress in this effort has lagged in comparison. Typically, machine translation consists of processing text from [...] Read more.
Given the achievements in automatically translating text from one language to another, one would expect to see similar advancements in translating between signed and spoken languages. However, progress in this effort has lagged in comparison. Typically, machine translation consists of processing text from one language to produce text in another. Because signed languages have no generally-accepted written form, translating spoken to signed language requires the additional step of displaying the language visually as animation through the use of a three-dimensional (3D) virtual human commonly known as an avatar. Researchers have been grappling with this problem for over twenty years, and it is still an open question. With the goal of developing a deeper understanding of the challenges posed by this question, this article gives a summary overview of the unique aspects of signed languages, briefly surveys the technology underlying avatars and performs an in-depth analysis of the features in a textual representation for avatar display. It concludes with a comparison of these features and makes observations about future research directions. Full article
(This article belongs to the Special Issue Frontiers in Machine Translation)
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16 pages, 2367 KiB  
Article
Medical Knowledge Graph Completion Based on Word Embeddings
by Mingxia Gao, Jianguo Lu and Furong Chen
Information 2022, 13(4), 205; https://doi.org/10.3390/info13040205 - 18 Apr 2022
Cited by 8 | Viewed by 3435
Abstract
The aim of Medical Knowledge Graph Completion is to automatically predict one of three parts (head entity, relationship, and tail entity) in RDF triples from medical data, mainly text data. Following their introduction, the use of pretrained language models, such as Word2vec, BERT, [...] Read more.
The aim of Medical Knowledge Graph Completion is to automatically predict one of three parts (head entity, relationship, and tail entity) in RDF triples from medical data, mainly text data. Following their introduction, the use of pretrained language models, such as Word2vec, BERT, and XLNET, to complete Medical Knowledge Graphs has become a popular research topic. The existing work focuses mainly on relationship completion and has rarely solved entities and related triples. In this paper, a framework to predict RDF triples for Medical Knowledge Graphs based on word embeddings (named PTMKG-WE) is proposed, for the specific use for the completion of entities and triples. The framework first formalizes existing samples for a given relationship from the Medical Knowledge Graph as prior knowledge. Second, it trains word embeddings from big medical data according to prior knowledge through Word2vec. Third, it can acquire candidate triples from word embeddings based on analogies from existing samples. In this framework, the paper proposes two strategies to improve the relation features. One is used to refine the relational semantics by clustering existing triple samples. Another is used to accurately embed the expression of the relationship through means of existing samples. These two strategies can be used separately (called PTMKG-WE-C and PTMKG-WE-M, respectively), and can also be superimposed (called PTMKG-WE-C-M) in the framework. Finally, in the current study, PubMed data and the National Drug File-Reference Terminology (NDF-RT) were collected, and a series of experiments was conducted. The experimental results show that the framework proposed in this paper and the two improvement strategies can be used to predict new triples for Medical Knowledge Graphs, when medical data are sufficiently abundant and the Knowledge Graph has appropriate prior knowledge. The two strategies designed to improve the relation features have a significant effect on the lifting precision, and the superposition effect becomes more obvious. Another conclusion is that, under the same parameter setting, the semantic precision of word embedding can be improved by extending the breadth and depth of data, and the precision of the prediction framework in this paper can be further improved in most cases. Thus, collecting and training big medical data is a viable method to learn more useful knowledge. Full article
(This article belongs to the Special Issue Knowledge Graph Technology and Its Applications)
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15 pages, 2590 KiB  
Article
Audio Storytelling Innovation in a Digital Age: The Case of Daily News Podcasts in Spain
by Leoz-Aizpuru Asier and Pedrero-Esteban Luis Miguel
Information 2022, 13(4), 204; https://doi.org/10.3390/info13040204 - 18 Apr 2022
Cited by 11 | Viewed by 4050
Abstract
On the 1st of February 2017, The New York Times published the first episode of ‘The Daily’, a news podcast hosted by Michael Barbaro that, five years later, has become the most popular in the world with four million listeners each day and [...] Read more.
On the 1st of February 2017, The New York Times published the first episode of ‘The Daily’, a news podcast hosted by Michael Barbaro that, five years later, has become the most popular in the world with four million listeners each day and more than 3000 million accumulated downloads. The unprecedented success of this audio format, that has emerged in a traditional newspaper, has helped to revamp radio news and has spread in various versions all over the world. This investigation analyses daily podcasts in Spain and, by means of a quantitative and qualitative study, identifies their main themes, narrative structures, and expressive contributions based on four illustrative experiences in this market: ‘Quién dice qué‘, ‘AM’, ‘El Mundo al día’, and ‘Un tema al día’. The results reveal the consolidation of two clearly defined models: a more conventional one based on radio bulletins and news reports; and another, more innovative model that replicates the anglophone formula that opts for depth, dissemination, and a conversational tone to redefine the canons of the audio news narrative and take advantage of the potential of audio as a new distribution channel for newspapers in the digital eco-system. Full article
(This article belongs to the Special Issue Advances in Interactive and Digital Media)
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26 pages, 609 KiB  
Review
Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision
by Hussan Munir, Bahtijar Vogel and Andreas Jacobsson
Information 2022, 13(4), 203; https://doi.org/10.3390/info13040203 - 17 Apr 2022
Cited by 31 | Viewed by 10419
Abstract
The use of artificial intelligence and machine learning techniques across all disciplines has exploded in the past few years, with the ever-growing size of data and the changing needs of higher education, such as digital education. Similarly, online educational information systems have a [...] Read more.
The use of artificial intelligence and machine learning techniques across all disciplines has exploded in the past few years, with the ever-growing size of data and the changing needs of higher education, such as digital education. Similarly, online educational information systems have a huge amount of data related to students in digital education. This educational data can be used with artificial intelligence and machine learning techniques to improve digital education. This study makes two main contributions. First, the study follows a repeatable and objective process of exploring the literature. Second, the study outlines and explains the literature’s themes related to the use of AI-based algorithms in digital education. The study findings present six themes related to the use of machines in digital education. The synthesized evidence in this study suggests that machine learning and deep learning algorithms are used in several themes of digital learning. These themes include using intelligent tutors, dropout predictions, performance predictions, adaptive and predictive learning and learning styles, analytics and group-based learning, and automation. artificial neural network and support vector machine algorithms appear to be utilized among all the identified themes, followed by random forest, decision tree, naive Bayes, and logistic regression algorithms. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Education)
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15 pages, 394 KiB  
Article
A Traffic-Load-Based Algorithm for Wireless Sensor Networks’ Lifetime Extension
by Georgios Tsoumanis, Nikolaos Giannakeas, Alexandros T. Tzallas, Evripidis Glavas, Kyriakos Koritsoglou, Evaggelos Karvounis, Konstantinos Bezas and Constantinos T. Angelis
Information 2022, 13(4), 202; https://doi.org/10.3390/info13040202 - 15 Apr 2022
Viewed by 1847
Abstract
It has been shown in the literature that the lifetime of a wireless sensor network is heavily connected to the number of transmissions that network nodes have to undertake. Considering this finding, along with the effects of the energy hole problem where nodes [...] Read more.
It has been shown in the literature that the lifetime of a wireless sensor network is heavily connected to the number of transmissions that network nodes have to undertake. Considering this finding, along with the effects of the energy hole problem where nodes closer to the sink node transmit more than the more distant ones, a node close to the sink node will be the one that transmits the most, while it will also be the node that will deplete its battery first. Taking into consideration that the failure of a single network node to operate, due to its battery being discharged, can lead to a network stopping its operation, the most energy-consuming node in the network will also be the one that will be responsible for the network’s termination. In this sense, the most energy-consuming node’s energy consumption optimization is the main case in this paper. More specifically, in this work, it is firstly shown that the energy consumption of a wireless sensor network is closely related to each network node’s traffic load, that is the transmissions of the packets that are created or forwarded by a node. The minimization of the most energy-consuming node’s energy consumption was studied here, while the implementation of a traffic-load-based algorithm is also proposed. Under the proposed algorithm, given a simple shortest path approach that assigns a parent (i.e., the next hop towards the sink node) in each network node and the knowledge it provides regarding the distance (in hops in this paper’s case) of network nodes from the sink node, the proposed algorithm exploits the shortest path’s results in order to discover, for all network nodes, neighbors that are of the same distance (from the sink node) with the initially assigned parent. Then, if such neighbors exist, all these neighbors are equally burdened with the parenting role. As a result, the traffic load is shared by all of them. To evaluate the proposed algorithm, simulation results are provided, showing that the goals set were achieved; thus, the network lifetime was prolonged. In addition, it is shown that under the algorithm, a fairer distribution of the traffic load takes place. Full article
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32 pages, 9129 KiB  
Article
A Framework for Online Public Health Debates: Some Design Elements for Visual Analytics Systems
by Anton Ninkov and Kamran Sedig
Information 2022, 13(4), 201; https://doi.org/10.3390/info13040201 - 15 Apr 2022
Viewed by 2730
Abstract
Nowadays, many people are deeply concerned about their physical well-being; as a result, they invest much time and effort investigating health-related topics. In response to this, many online websites and social media profiles have been created, resulting in a plethora of information on [...] Read more.
Nowadays, many people are deeply concerned about their physical well-being; as a result, they invest much time and effort investigating health-related topics. In response to this, many online websites and social media profiles have been created, resulting in a plethora of information on such topics. In a given topic, oftentimes, much of the information is conflicting, resulting in online camps that have different positions and arguments. We refer to the collection of all such positionings and entrenched camps on a topic as an online public health debate. The information people encounter regarding such debates can ultimately influence how they make decisions, what they believe, and how they act. Therefore, there is a need for public health stakeholders (i.e., people with a vested interest in public health issues) to be able to make sense of online debates quickly and accurately. In this paper, we present a framework-based approach for investigating online public health debates—a preliminary work that can be expanded upon. We first introduce the concept of online debate entities (ODEs), which is a generalization for those who participate in online debates (e.g., websites and Twitter profiles). We then present the framework ODIN (Online Debate entIty aNalyzer), in which we identify, define, and justify ODE attributes that we consider important for making sense of online debates. Next, we provide an overview of four online public health debates (vaccines, statins, cannabis, and dieting plans) using ODIN. Finally, we showcase four prototype visual analytics systems whose design elements are informed by the ODIN framework. Full article
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22 pages, 3441 KiB  
Article
User Pairing and Power Allocation for NOMA-CoMP Based on Rate Prediction
by Jiang Wu, Leyang Sun and Yubo Jia
Information 2022, 13(4), 200; https://doi.org/10.3390/info13040200 - 15 Apr 2022
Cited by 2 | Viewed by 2089
Abstract
In this paper, we consider a non-orthogonal multiple access (NOMA) system with coordinated multi-point (CoMP), which is used in 5G cellular networks to guarantee the rate requirements from the different edge users. Based on the China Family Panel Studies (CFPS) dataset, we use [...] Read more.
In this paper, we consider a non-orthogonal multiple access (NOMA) system with coordinated multi-point (CoMP), which is used in 5G cellular networks to guarantee the rate requirements from the different edge users. Based on the China Family Panel Studies (CFPS) dataset, we use several learning algorithms to predict users’ rate requirements according to their profiles. We propose a many-to-many two-side subchannel–user matching strategy, which can classify users into cell-center users, high-rate requirement edge users, and low-rate requirement edge users based on their status and learning prediction results, and pair users with different subchannels to form joint transmission CoMP (JT-CoMP) subchannels and dynamic point selection CoMP (DPS-CoMP) subchannels. Furthermore, a discrete power allocation algorithm based on group search is proposed. Simulation results show that our proposed algorithm outperforms the traditional NOMA-CoMP algorithm and maximum throughput (MT) NOMA-CoMP algorithm. It maximizes the rate of high-rate requirement edge users while guaranteeing user fairness. Full article
(This article belongs to the Special Issue Wireless Communications, Networking and Applications)
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16 pages, 266 KiB  
Article
Adopting AI in the Context of Knowledge Work: Empirical Insights from German Organizations
by Georg von Richthofen, Shirley Ogolla and Hendrik Send
Information 2022, 13(4), 199; https://doi.org/10.3390/info13040199 - 15 Apr 2022
Cited by 3 | Viewed by 4136
Abstract
Artificial Intelligence (AI) is increasingly adopted by organizations. In general, scholars agree that the adoption of AI will be associated with substantial changes in the workplace. Empirical evidence on the phenomenon remains scarce, however. In this article, we explore the adoption of AI [...] Read more.
Artificial Intelligence (AI) is increasingly adopted by organizations. In general, scholars agree that the adoption of AI will be associated with substantial changes in the workplace. Empirical evidence on the phenomenon remains scarce, however. In this article, we explore the adoption of AI in the context of knowledge work. Drawing on case study research in eight German organizations that have either implemented AI or are in the process of developing AI systems, we identify three pervasive changes that knowledge workers perceive: a shift from manual labor and repetitive tasks to tasks that involve reasoning and empathy, an emergence of new tasks and roles, and an emergence of new skill requirements. In addition, we identify three factors that are conducive to the development of AI systems in the context of knowledge work: leadership support, participative change management, and effective integration of domain knowledge. Theoretical and managerial implications are discussed. Full article
19 pages, 7502 KiB  
Article
Local Transformer Network on 3D Point Cloud Semantic Segmentation
by Zijun Wang, Yun Wang, Lifeng An, Jian Liu and Haiyang Liu
Information 2022, 13(4), 198; https://doi.org/10.3390/info13040198 - 14 Apr 2022
Cited by 4 | Viewed by 2825
Abstract
Semantic segmentation is an important component in understanding the 3D point cloud scene. Whether we can effectively obtain local and global contextual information from points is of great significance in improving the performance of 3D point cloud semantic segmentation. In this paper, we [...] Read more.
Semantic segmentation is an important component in understanding the 3D point cloud scene. Whether we can effectively obtain local and global contextual information from points is of great significance in improving the performance of 3D point cloud semantic segmentation. In this paper, we propose a self-attention feature extraction module: the local transformer structure. By stacking the encoder layer composed of this structure, we can extract local features while preserving global connectivity. The structure can automatically learn each point feature from its neighborhoods and is invariant to different point orders. We designed two unique key matrices, each of which focuses on the feature similarities and geometric structure relationships between the points to generate attention weight matrices. Additionally, the cross-skip selection of neighbors is used to obtain larger receptive fields for each point without increasing the number of calculations required, and can therefore better deal with the junction between multiple objects. When the new network was verified on the S3DIS, the mean intersection over union was 69.1%, and the segmentation accuracies on the complex outdoor scene datasets Semantic3D and SemanticKITTI were 94.3% and 87.8%, respectively, which demonstrate the effectiveness of the proposed methods. Full article
(This article belongs to the Topic Big Data and Artificial Intelligence)
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16 pages, 2235 KiB  
Article
An Effective Student Grouping and Course Recommendation Strategy Based on Big Data in Education
by Yu Guo, Yue Chen, Yuanyan Xie and Xiaojuan Ban
Information 2022, 13(4), 197; https://doi.org/10.3390/info13040197 - 14 Apr 2022
Cited by 5 | Viewed by 2298
Abstract
Personalized education aims to provide cooperative and exploratory courses for students by using computer and network technology to construct a more effective cooperative learning mode, thus improving students’ cooperation ability and lifelong learning ability. Based on students’ interests, this paper proposes an effective [...] Read more.
Personalized education aims to provide cooperative and exploratory courses for students by using computer and network technology to construct a more effective cooperative learning mode, thus improving students’ cooperation ability and lifelong learning ability. Based on students’ interests, this paper proposes an effective student grouping strategy and group-oriented course recommendation method, comprehensively considering characteristics of students and courses both from a statistical dimension and a semantic dimension. First, this paper combines term frequency–inverse document frequency and Word2Vec to preferably extract student characteristics. Then, an improved K-means algorithm is used to divide students into different interest-based study groups. Finally, the group-oriented course recommendation method recommends appropriate and quality courses according to the similarity and expert score. Based on real data provided by junior high school students, a series of experiments are conducted to recommend proper social practical courses, which verified the feasibility and effectiveness of the proposed strategy. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Education)
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14 pages, 470 KiB  
Article
Digital Transformation Strategies Enabled by Internet of Things and Big Data Analytics: The Use-Case of Telecommunication Companies in Greece
by Ilias Moumtzidis, Maria Kamariotou and Fotis Kitsios
Information 2022, 13(4), 196; https://doi.org/10.3390/info13040196 - 14 Apr 2022
Cited by 11 | Viewed by 4114
Abstract
Both Internet of Things (IoT) and Big Data Analytics (BDA) are innovations that already caused a significant disruption having a major impact on organizations. To reduce the attrition of new technology implementation, it is critical to examine the advantages of BDA and the [...] Read more.
Both Internet of Things (IoT) and Big Data Analytics (BDA) are innovations that already caused a significant disruption having a major impact on organizations. To reduce the attrition of new technology implementation, it is critical to examine the advantages of BDA and the determinants that have a detrimental or positive impact on users’ attitudes toward information systems. This article aims to evaluate the intention to use and the perceived benefits of BDA systems and IoT in the telecommunication industry. The research is based on the Technology Acceptance Model (TAM). Data were collected by 172 users and analyzed using Multivariate Regression Analysis. From our findings, we may draw some important lessons about how to increase the adoption of new technology and conventional practices while also considering a variety of diverse aspects. Users will probably use both systems if they think they will be valuable and easy to use. Regarding BDA, the good quality of data helps users see the system’s benefits, while regarding IoT, the high quality of the services is the most important thing. Full article
(This article belongs to the Special Issue Big Data, IoT and Cloud Computing)
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15 pages, 1897 KiB  
Article
Earthquake Detection at the Edge: IoT Crowdsensing Network
by Enrico Bassetti and Emanuele Panizzi
Information 2022, 13(4), 195; https://doi.org/10.3390/info13040195 - 13 Apr 2022
Cited by 10 | Viewed by 3287
Abstract
State-of-the-art Earthquake Early Warning systems rely on a network of sensors connected to a fusion center in a client–server paradigm. The fusion center runs different algorithms on the whole data set to detect earthquakes. Instead, we propose moving computation to the edge, with [...] Read more.
State-of-the-art Earthquake Early Warning systems rely on a network of sensors connected to a fusion center in a client–server paradigm. The fusion center runs different algorithms on the whole data set to detect earthquakes. Instead, we propose moving computation to the edge, with detector nodes that probe the environment and process information from nearby probes to detect earthquakes locally. Our approach tolerates multiple node faults and partial network disruption and keeps all data locally, enhancing privacy. This paper describes our proposal’s rationale and explains its architecture. We then present an implementation that uses Raspberry, NodeMCU, and the Crowdquake machine learning model. Full article
(This article belongs to the Special Issue Artificial Intelligence on the Edge)
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15 pages, 548 KiB  
Article
Low Power EEG Data Encoding for Brain Neurostimulation Implants
by Aikaterini Fragkou, Athanasios Kakarountas and Vasileios Kokkinos
Information 2022, 13(4), 194; https://doi.org/10.3390/info13040194 - 12 Apr 2022
Cited by 1 | Viewed by 1909
Abstract
Neurostimulation devices applied for the treatment of epilepsy that collect, encode, temporarily store, and transfer electroencephalographic (EEG) signals recorded intracranially from epileptic patients, suffer from short battery life spans. The principal goal of this study is to implement strategies for low power consumption [...] Read more.
Neurostimulation devices applied for the treatment of epilepsy that collect, encode, temporarily store, and transfer electroencephalographic (EEG) signals recorded intracranially from epileptic patients, suffer from short battery life spans. The principal goal of this study is to implement strategies for low power consumption rates during the device’s smooth and uninterrupted operation as well as during data transmission. Our approach is organised in three basic levels. The first level regards the initial modelling and creation of the template for the following two stages. The second level regards the development of code for programming integrated circuits and simulation. The third and final stage regards the transmitter’s implementation at the evaluation level. In particular, more than one software and device are involved in this phase, in order to achieve realistic performance. Our research aims to evolve such technologies so that they can transmit wireless data with simultaneous energy efficiency. Full article
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28 pages, 1064 KiB  
Article
Instruments and Tools to Identify Radical Textual Content
by Josiane Mothe, Md Zia Ullah, Guenter Okon, Thomas Schweer, Alfonsas Juršėnas and Justina Mandravickaitė
Information 2022, 13(4), 193; https://doi.org/10.3390/info13040193 - 12 Apr 2022
Cited by 2 | Viewed by 2198
Abstract
The Internet and social networks are increasingly becoming a media of extremist propaganda. On homepages, in forums or chats, extremists spread their ideologies and world views, which are often contrary to the basic liberal democratic values of the European Union. It is not [...] Read more.
The Internet and social networks are increasingly becoming a media of extremist propaganda. On homepages, in forums or chats, extremists spread their ideologies and world views, which are often contrary to the basic liberal democratic values of the European Union. It is not uncommon that violence is used against those of different faiths, those who think differently, and members of social minorities. This paper presents a set of instruments and tools developed to help investigators to better address hybrid security threats, i.e., threats that combine physical and cyber attacks. These tools have been designed and developed to support security authorities in identifying extremist propaganda on the Internet and classifying it in terms of its degree of danger. This concerns both extremist content on freely accessible Internet pages and content in closed chats. We illustrate the functionalities of the tools through an example related to radicalisation detection; the data used here are just a few tweets, emails propaganda, and darknet posts. This work was supported by the EU granted PREVISION (Prediction and Visual Intelligence for Security Intelligence) project. Full article
(This article belongs to the Special Issue Predictive Analytics and Illicit Activities)
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14 pages, 273 KiB  
Article
Institutional Strategies for Cybersecurity in Higher Education Institutions
by Eric C. K. Cheng and Tianchong Wang
Information 2022, 13(4), 192; https://doi.org/10.3390/info13040192 - 12 Apr 2022
Cited by 13 | Viewed by 9187
Abstract
Cybersecurity threats have grown exponentially, posing a heavy burden on organisations. Higher Education Institutions (HEIs) are particularly vulnerable, and their cybersecurity issues are receiving greater attention. However, existing research on cybersecurity has limited referencing value for HEI leaders and policy-makers because they are [...] Read more.
Cybersecurity threats have grown exponentially, posing a heavy burden on organisations. Higher Education Institutions (HEIs) are particularly vulnerable, and their cybersecurity issues are receiving greater attention. However, existing research on cybersecurity has limited referencing value for HEI leaders and policy-makers because they are usually technology-focused. Publications that showcase best practices often lack system-wide perspectives towards cybersecurity in HEIs. Our paper, therefore, aims to bridge this literature gap and generate institutional cybersecurity strategies for HEI leaders and policy-makers from a system perspective. We first review how the cybersecurity landscape has evolved over the last few decades and its latest trends and projections for the next decade. By analysing these historical developments and new changes, we further illuminate the importance of strengthening HEI cybersecurity capacities. As we explore why HEIs face severe challenges to tackle the ever-escalating cyberattacks, we propose a system-wide approach to safeguard HEI cybersecurity and highlight the necessity to reassess prioritised areas. By taking an extensive literature review and desk research of methods that could respond to the cybersecurity vulnerabilities of the next decade, we synthesise our findings with a set of institutional strategies, with takeaways designed to equip HEIs better to address cybersecurity threats into the future. The strategies include: (1) Strengthening Institutional Governance for Cybersecurity; (2) Revisiting Cybersecurity KPIs; (3) Explicating Cybersecurity Policies, Guidelines and Mechanisms; (4) Training and Cybersecurity Awareness Campaigns to Build Cybersecurity Culture; (5) Responding to AI-based Cyber-threats and Harnessing AI to Enhance Cybersecurity; (6) Introduction of New and More Sophisticated Security Measures; (7) Paying Attention to Mobile Devices Use, Using Encryption as a Daily Practice; and (8) Risk Management. We believe that cybersecurity can be safeguarded throughout the new decade when these strategies are considered thoroughly and with the concerted effort of relevant HEI stakeholders. Full article
(This article belongs to the Special Issue Information Technologies in Education, Research and Innovation)
19 pages, 791 KiB  
Article
Coded Caching for Combination Networks with Multiaccess
by Leitang Huang, Jinyu Wang, Minquan Cheng, Qingyong Deng and Bineng Zhong
Information 2022, 13(4), 191; https://doi.org/10.3390/info13040191 - 11 Apr 2022
Viewed by 1613
Abstract
In a traditional (H,r) combination network, each user connects to a unique set of r relays. However, few research efforts have considered the (H,r,u) multiaccess combination network problem wherein each unique set of [...] Read more.
In a traditional (H,r) combination network, each user connects to a unique set of r relays. However, few research efforts have considered the (H,r,u) multiaccess combination network problem wherein each unique set of r relays is connected by u users. In this paper, we focus on designing coded caching schemes for a (H,r,u) multiaccess combination network. By directly applying the well-known coding method (proposed by Zewail and Yener) for a (H,r) combination network, a coded caching scheme (called ZY scheme) for (H,r,u) multiaccess combination network is obtained. However, its subpacketization has an exponential order with the number of users which leads to high implementation complexity. In order to reduce subpacketization, a direct construction of a coded caching scheme (called the direct scheme) for (H,r,u) multiaccess combination network is proposed by means of combinational design theory, where the parameter u must be a combinatorial number. For the arbitrary parameter u, the hybrid construction of a coded caching scheme (called the hybrid scheme) for the (H,r,u) multiaccess combination network is proposed based on the direct scheme. Theoretical and numerical analysis shows that the direct scheme and the hybrid scheme have a smaller transmission load for each relay compared with the naive scheme (which is obtained by repeatedly applying the coded caching scheme for a traditional (H,r) combination network by u times) and have much lower subpacketization compared with the ZY scheme. Full article
(This article belongs to the Special Issue Advanced Technologies in Storage, Computing, and Communication)
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16 pages, 774 KiB  
Article
Performance Evaluation of Distributed Database Strategies Using Docker as a Service for Industrial IoT Data: Application to Industry 4.0
by Theodosios Gkamas, Vasileios Karaiskos and Sotirios Kontogiannis
Information 2022, 13(4), 190; https://doi.org/10.3390/info13040190 - 9 Apr 2022
Cited by 10 | Viewed by 4145
Abstract
Databases are an integral part of almost every application nowadays. For example, applications using Internet of Things (IoT) sensory data, such as in Industry 4.0, are a classic example of an organized storage system. Due to its enormous size, it may be stored [...] Read more.
Databases are an integral part of almost every application nowadays. For example, applications using Internet of Things (IoT) sensory data, such as in Industry 4.0, are a classic example of an organized storage system. Due to its enormous size, it may be stored in the cloud. This paper presents the authors’ proposition for cloudcentric sensory measurements and measurements acquisition. Then, it focuses on evaluating industrial cloud storage engines for sensory functions, experimenting with three open-source types of distributed Database Management Systems (DBMS); MongoDB and PostgreSQL, with two forms of PostgreSQL schemes (Javascript Object Notation (JSON)-based and relational), against their respective horizontal scaling strategies. Several experimental cases have been performed to measure database queries’ response time, achieved throughput, and corresponding failures. Three distinct scenarios have been thoroughly tested, the most common but widely used: (i) data insertions, (ii) select/find queries, and (iii) queries related to aggregate correlation functions. The experimental results concluded that PostgreSQL with JSON achieves a 5–57% better response than MongoDB for the insert queries (cases of native, two, and four shards implementations), while, on the contrary, MongoDB achieved 56–91% higher throughput than PostgreSQL for the same set up. Furthermore, for the data insertion experimental cases of six and eight shards, MongoDB performed 13–20% more than Postgres in response time, achieving × 2 times higher throughput. Relational PostgreSQL was × 2 times faster than MongoDB in its standalone implementation for selection queries. At the same time, MongoDB achieved 19–31% faster responses and 44–63% higher throughput than PostgreSQL in the four tested sharding subcases (two, four, six, eight shards), accordingly. Finally, the relational PostgreSQL outperformed MongoDB and PostgreSQL JSON significantly in all correlation function experiments, with performance improvements from MongoDB, closing the gap with PostgreSQL towards minimizing response time to 26% and 3% for six and eight shards, respectively, and achieving significant gains towards average achieved throughput. Full article
(This article belongs to the Special Issue Big Data, IoT and Cloud Computing)
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19 pages, 3472 KiB  
Article
Identifying Adverse Drug Reaction-Related Text from Social Media: A Multi-View Active Learning Approach with Various Document Representations
by Jing Liu, Yue Wang, Lihua Huang, Chenghong Zhang and Songzheng Zhao
Information 2022, 13(4), 189; https://doi.org/10.3390/info13040189 - 8 Apr 2022
Cited by 1 | Viewed by 1828
Abstract
Adverse drug reactions (ADRs) are a huge public health issue. Identifying text that mentions ADRs from a large volume of social media data is important. However, we need to address two challenges for high-performing ADR-related text detection: the data imbalance problem and the [...] Read more.
Adverse drug reactions (ADRs) are a huge public health issue. Identifying text that mentions ADRs from a large volume of social media data is important. However, we need to address two challenges for high-performing ADR-related text detection: the data imbalance problem and the requirement of simultaneously using data-driven information and handcrafted information. Therefore, we propose an approach named multi-view active learning using domain-specific and data-driven document representations (MVAL4D), endeavoring to enhance the predictive capability and alleviate the requirement of labeled data. Specifically, a new view-generation mechanism is proposed to generate multiple views by simultaneously exploiting various document representations obtained using handcrafted feature engineering and by performing deep learning methods. Moreover, different from previous active learning studies in which all instances are chosen using the same selection criterion, MVAL4D adopts different criteria (i.e., confidence and informativeness) to select potentially positive instances and potentially negative instances for manual annotation. The experimental results verify the effectiveness of MVAL4D. The proposed approach can be generalized to many other text classification tasks. Moreover, it can offer a solid foundation for the ADR mention extraction task, and improve the feasibility of monitoring drug safety using social media data. Full article
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12 pages, 1403 KiB  
Article
Recommendation System Algorithms on Location-Based Social Networks: Comparative Study
by Abeer Al-Nafjan, Norah Alrashoudi and Hend Alrasheed
Information 2022, 13(4), 188; https://doi.org/10.3390/info13040188 - 8 Apr 2022
Cited by 4 | Viewed by 3633
Abstract
Currently, social networks allow individuals from all over the world to share ideas, activities, events, and interests over the Internet. Using location-based social networks (LBSNs), users can share their locations and location-related content, including images and reviews. Location rec-14 recommendation system-based LBSN has [...] Read more.
Currently, social networks allow individuals from all over the world to share ideas, activities, events, and interests over the Internet. Using location-based social networks (LBSNs), users can share their locations and location-related content, including images and reviews. Location rec-14 recommendation system-based LBSN has gained considerable attention in research using techniques and methods based on users’ geosocial activities. In this study, we present a comparative analysis of three matrix factorization (MF) algorithms, namely, singular value decomposition (SVD), singular value decomposition plus (SVD++), and nonnegative matrix factorization (NMF). The primary task of the implemented recommender system was to predict restaurant ratings for each user and make a recommendation based on this prediction. This experiment used two performance metrics for evaluation, namely, root mean square error (RMSE) and mean absolute error (MAE). The RMSEs confirmed the efficacy of SVD with a lower error rate, whereas SVD++ had a lower error rate in terms of MAE. Full article
(This article belongs to the Special Issue Information Retrieval, Recommender Systems and Adaptive Systems)
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21 pages, 19951 KiB  
Article
Model-Based Underwater Image Simulation and Learning-Based Underwater Image Enhancement Method
by Yidan Liu, Huiping Xu, Bing Zhang, Kelin Sun, Jingchuan Yang, Bo Li, Chen Li and Xiangqian Quan
Information 2022, 13(4), 187; https://doi.org/10.3390/info13040187 - 7 Apr 2022
Cited by 9 | Viewed by 2717
Abstract
Due to the absorption and scattering effects of light in water bodies and the non-uniformity and insufficiency of artificial illumination, underwater images often present various degradation problems, impacting their utility in underwater applications. In this paper, we propose a model-based underwater image simulation [...] Read more.
Due to the absorption and scattering effects of light in water bodies and the non-uniformity and insufficiency of artificial illumination, underwater images often present various degradation problems, impacting their utility in underwater applications. In this paper, we propose a model-based underwater image simulation and learning-based underwater image enhancement method for coping with various degradation problems in underwater images. We first derive a simplified model for describing various degradation problems in underwater images, then propose a model-based image simulation method that can generate images with a wide range of parameter values. The proposed image simulation method also comes with an image-selection part, which helps to prune the simulation dataset so that it can serve as a training set for learning to enhance the targeted underwater images. Afterwards, we propose a convolutional neural network based on the encoder-decoder backbone to learn to enhance various underwater images from the simulated images. Experiments on simulated and real underwater images with different degradation problems demonstrate the effectiveness of the proposed underwater image simulation and enhancement method, and reveal the advantages of the proposed method in comparison with many state-of-the-art methods. Full article
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16 pages, 4125 KiB  
Article
A Personalized Compression Method for Steady-State Visual Evoked Potential EEG Signals
by Sitao Zhang, Kainan Ma, Yibo Yin, Binbin Ren and Ming Liu
Information 2022, 13(4), 186; https://doi.org/10.3390/info13040186 - 6 Apr 2022
Cited by 3 | Viewed by 1934
Abstract
As an informative electroencephalogram (EEG) signal, steady-state visual evoked potential (SSVEP) stands out from many paradigms for application in wireless wearable devices. However, its data are usually enormous, occupy too many bandwidth sources and require immense power when transmitted in the raw data [...] Read more.
As an informative electroencephalogram (EEG) signal, steady-state visual evoked potential (SSVEP) stands out from many paradigms for application in wireless wearable devices. However, its data are usually enormous, occupy too many bandwidth sources and require immense power when transmitted in the raw data form, so it is necessary to compress the signal. This paper proposes a personalized EEG compression and reconstruction algorithm for the SSVEP application. In the algorithm, to realize personalization, a primary artificial neural network (ANN) model is first pre-trained with the open benchmark database towards BCI application (BETA). Then, an adaptive ANN model is generated with incremental learning for each subject to compress their individual data. Additionally, a personalized, non-uniform quantization method is proposed to reduce the errors caused by compression. The recognition accuracy only decreases by 3.79% when the compression rate is 12.7 times, and is tested on BETA. The proposed algorithm can reduce signal loss by from 50.43% to 81.08% in the accuracy test compared to the case without ANN and uniform quantization. Full article
(This article belongs to the Special Issue Biomedical Signal Processing and Data Analytics in Healthcare Systems)
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9 pages, 963 KiB  
Article
Business Process Re-Engineering: A Literature Review-Based Analysis of Implementation Measures
by Aljazzi Fetais, Galal M. Abdella, Khalifa N. Al-Khalifa and Abdel Magid Hamouda
Information 2022, 13(4), 185; https://doi.org/10.3390/info13040185 - 5 Apr 2022
Cited by 10 | Viewed by 11833
Abstract
Business process re-engineering (BPR) is an approach to improving organizational performance. It evolved mostly within the private sector to maintain a successful business model despite increasing global competition. BPR presents a fundamental improvement in the essential organizational design. This paper investigates recent studies [...] Read more.
Business process re-engineering (BPR) is an approach to improving organizational performance. It evolved mostly within the private sector to maintain a successful business model despite increasing global competition. BPR presents a fundamental improvement in the essential organizational design. This paper investigates recent studies of BPR and identifies the success factors of BPR projects and their connection to the human–technology–organization (HTO) framework. By examining the relevant literature, we study various factors and their effects on the implementation of BPR and how these factors can affect process performance, successfully or otherwise. The aim is to study the literature to determine the success factors and challenges for BPR in the HTO framework. The article concludes by emphasizing the factors that will help to allow BPR to be implemented with a wider use in different sectors. Full article
(This article belongs to the Special Issue Business Process Management)
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20 pages, 6378 KiB  
Article
Fast Tracking Algorithm Based on Spatial Regularization Correlation Filter
by Caihong Liu, Mayire Ibrayim and Askar Hamdulla
Information 2022, 13(4), 184; https://doi.org/10.3390/info13040184 - 2 Apr 2022
Viewed by 2427
Abstract
To solve the problem of the redundant number of training samples in a correlation filter-based tracking algorithm, the training samples were implicitly extended by circular shifts of the given target patches, and all the extended samples were used as negative samples for the [...] Read more.
To solve the problem of the redundant number of training samples in a correlation filter-based tracking algorithm, the training samples were implicitly extended by circular shifts of the given target patches, and all the extended samples were used as negative samples for the fast online learning of the filter. Since all these shifted patches were not true negative samples of the target, the tracking process suffered from boundary effects, especially in challenging situations such as occlusion and background clutter, which can significantly impair the tracking performance of the tracker. Spatial regularization in the SRDCF tracking algorithm is an effective way to mitigate boundary effects, but it comes at the cost of highly increased time complexity, resulting in a very slow tracking speed of the SRDCF algorithm that cannot achieve a real-time tracking effect. To address this issue, we proposed a fast-tracking algorithm based on spatially regularized correlation filters that efficiently optimized the solved filters by replacing the Gauss–Seidel method in the SRDCF algorithm with the alternating direction multiplier method. The problem of slow speed in the SRDCF tracking algorithm improved, and the improved FSRCF algorithm achieved real-time tracking. An adaptive update mechanism was proposed by using the feedback from the high confidence tracking results to avoid model corruption. That is, a robust confidence evaluation criterion was introduced in the model update phase, which combined the maximum response criterion and the average peak correlation energy APCE criterion to determine whether to update the filter, thereby avoiding filter model drift and improving the target tracking accuracy and speed. We conducted extensive experiments on datasets OTB-2015, OTB-2013, UAV123, and TC128, and the experimental results show that the proposed algorithm exhibits a more stable and accurate tracking performance in the presence of occlusion and background clutter during tracking. Full article
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17 pages, 2127 KiB  
Article
A Game Theory Approach for Assisting Humans in Online Information-Sharing
by Ron S. Hirschprung and Shani Alkoby
Information 2022, 13(4), 183; https://doi.org/10.3390/info13040183 - 2 Apr 2022
Viewed by 2340
Abstract
Contemporary information-sharing environments such as Facebook offer a wide range of social and practical benefits. These environments, however, may also lead to privacy and security violations. Moreover, there is usually a trade-off between the benefits gained and the accompanying costs. Due to the [...] Read more.
Contemporary information-sharing environments such as Facebook offer a wide range of social and practical benefits. These environments, however, may also lead to privacy and security violations. Moreover, there is usually a trade-off between the benefits gained and the accompanying costs. Due to the uncertain nature of the information-sharing environment and the lack of technological literacy, the layperson user often fails miserably in balancing this trade-off. In this paper, we use game theory concepts to formally model this problem as a “game”, in which the players are the users and the pay-off function is a combination of the benefits and costs of the information-sharing process. We introduce a novel theoretical framework called Online Information-Sharing Assistance (OISA) to evaluate the interactive nature of the information-sharing trade-off problem. Using these theoretical foundations, we develop a set of AI agents that attempt to calculate a strategy for balancing this trade-off. Finally, as a proof of concept, we conduct an empirical study in a simulated Facebook environment in which human participants compete against OISA-based AI agents, showing that significantly higher utility can be achieved using OISA. Full article
(This article belongs to the Section Information Security and Privacy)
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19 pages, 2373 KiB  
Article
Multicriteria Approach for Design Optimization of Lightweight Piezoelectric Energy Harvesters Subjected to Stress Constraints
by Georgia Foutsitzi, Christos Gogos, Nikolaos Antoniadis and Aris Magklaras
Information 2022, 13(4), 182; https://doi.org/10.3390/info13040182 - 2 Apr 2022
Cited by 1 | Viewed by 1940
Abstract
In this work a multicriteria optimization approach to minimize weight and maximize power output in piezoelectric energy harvesting systems for aerospace applications is studied. The design variables are the geometric and electric circuit parameters of the vibration-based piezoelectric energy harvester (PEH). A finite [...] Read more.
In this work a multicriteria optimization approach to minimize weight and maximize power output in piezoelectric energy harvesting systems for aerospace applications is studied. The design variables are the geometric and electric circuit parameters of the vibration-based piezoelectric energy harvester (PEH). A finite element model is developed to model the dynamic behavior of the composite plate-type harvester with embedded piezoelectric layers. The cantilever PEH structure is subjected to constraints in the bending stresses which must be lower than or equal to the tensile yield strength of the piezoelectric material. For solving the multi-objective optimization problem, the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the Non-dominated Sorting Genetic Algorithm III (NSGA-III) and the Generalized Differential Evolution 3 (GDE3) algorithm are employed. It is shown that the proposed algorithms are effective in developing optimal Pareto front curves for maximum electrical power output and minimum mass of the PEH system. A comparative assessment of the solutions generated on the Pareto Front show that GDE3 achieved solutions that generate higher maximum power output and performs better compared to the two other algorithms. Full article
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19 pages, 757 KiB  
Article
A Novel Epidemic Model for the Interference Spread in the Internet of Things
by Emmanuel Tuyishimire, Jean de Dieu Niyigena, Fidèle Mweruli Tubanambazi, Justin Ushize Rutikanga, Paul Gatabazi, Antoine Bagula and Emmanuel Niyigaba
Information 2022, 13(4), 181; https://doi.org/10.3390/info13040181 - 2 Apr 2022
Cited by 1 | Viewed by 2271
Abstract
Due to the multi-technology advancements, internet of things (IoT) applications are in high demand to create smarter environments. Smart objects communicate by exchanging many messages, and this creates interference on receivers. Collection tree algorithms are applied to only reduce the nodes/paths’ interference but [...] Read more.
Due to the multi-technology advancements, internet of things (IoT) applications are in high demand to create smarter environments. Smart objects communicate by exchanging many messages, and this creates interference on receivers. Collection tree algorithms are applied to only reduce the nodes/paths’ interference but cannot fully handle the interference across the underlying IoT. This paper models and analyzes the interference spread in the IoT setting, where the collection tree routing algorithm is adopted. Node interference is treated as a real-life contamination of a disease, where individuals can migrate across compartments such as susceptible, attacked and replaced. The assumed typical collection tree routing model is the least interference beaconing algorithm (LIBA), and the dynamics of the interference spread is studied. The underlying network’s nodes are partitioned into groups of nodes which can affect each other and based on the partition property, the susceptible–attacked–replaced (SAR) model is proposed. To analyze the model, the system stability is studied, and the compartmental based trends are experimented in static, stochastic and predictive systems. The results shows that the dynamics of the system are dependent groups and all have points of convergence for static, stochastic and predictive systems. Full article
(This article belongs to the Special Issue Information Spreading on Networks)
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13 pages, 1983 KiB  
Article
Deep Learning with Word Embedding Improves Kazakh Named-Entity Recognition
by Gulizada Haisa and Gulila Altenbek
Information 2022, 13(4), 180; https://doi.org/10.3390/info13040180 - 2 Apr 2022
Cited by 4 | Viewed by 2768
Abstract
Named-entity recognition (NER) is a preliminary step for several text extraction tasks. In this work, we try to recognize Kazakh named entities by introducing a hybrid neural network model that leverages word semantics with multidimensional features and attention mechanisms. There are two major [...] Read more.
Named-entity recognition (NER) is a preliminary step for several text extraction tasks. In this work, we try to recognize Kazakh named entities by introducing a hybrid neural network model that leverages word semantics with multidimensional features and attention mechanisms. There are two major challenges: First, Kazakh is an agglutinative and morphologically rich language that presents a challenge for NER due to data sparsity. The other is that Kazakh named entities have unclear boundaries, polysemy, and nesting. A common strategy to handle data sparsity is to apply subword segmentation. Thus, we combined the semantics of words and stems by stemming from the Kazakh morphological analysis system. Additionally, we constructed a graph structure of entities, with words, entities, and entity categories as nodes and inclusion relations as edges, and updated nodes using a gated graph neural network (GGNN) with an attention mechanism. Finally, through the conditional random field (CRF), we extracted the final results. Experimental results show that our method consistently outperforms all previous methods by 88.04% in terms of F1 scores. Full article
(This article belongs to the Topic Big Data and Artificial Intelligence)
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10 pages, 295 KiB  
Article
Study of Transformed ηζ Networks via Zagreb Connection Indices
by Muhammad Hussain, Atiq ur Rehman, Andrii Shekhovtsov, Muhammad Asif and Wojciech Sałabun
Information 2022, 13(4), 179; https://doi.org/10.3390/info13040179 - 31 Mar 2022
Viewed by 1767
Abstract
A graph is a tool for designing a system’s required interconnection network. The topology of such networks determines their compatibility. For the first time, in this work we construct subdivided ηζ network S(ηζΓ) and discussed their topology. [...] Read more.
A graph is a tool for designing a system’s required interconnection network. The topology of such networks determines their compatibility. For the first time, in this work we construct subdivided ηζ network S(ηζΓ) and discussed their topology. In graph theory, there are a variety of invariants to study the topology of a network, but topological indices are designed in such a way that these may transform the graph into a numeric value. In this work, we study S(ηζΓ) via Zagreb connection indices. Due to their predictive potential for enthalpy, entropy, and acentric factor, these indices gain value in the field of chemical graph theory in a very short time. ηζΓ formed by ζ time repeated process which consists ηζ copies of graph Γ along with η2|V(Γ)|ζηζ1 edges which used to join these ηζ copies of Γ. The free hand to choose the initial graph Γ for desired network S(ηζΓ) and its relation with chemical networks along with the repute of Zagreb connection indices enhance the worth of this study. These computations are theoretically innovative and aid topological characterization of S(ηζΓ). Full article
(This article belongs to the Special Issue Artificial Intelligence and Decision Support Systems)
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27 pages, 2902 KiB  
Article
Value and Sustainability of Emerging Social Commerce Professions: An Exploratory Study
by Krassie Petrova and Sagorika Datta
Information 2022, 13(4), 178; https://doi.org/10.3390/info13040178 - 31 Mar 2022
Cited by 2 | Viewed by 3058
Abstract
Recent advances in social commerce and mobile technology have led to the emergence of new professions such as vlogging, blogging and virtual pop-up store owning. Starting initially as hobbies, the services provided by these ‘new professionals’ have become ubiquitous and are being used [...] Read more.
Recent advances in social commerce and mobile technology have led to the emergence of new professions such as vlogging, blogging and virtual pop-up store owning. Starting initially as hobbies, the services provided by these ‘new professionals’ have become ubiquitous and are being used by customers from many different countries and backgrounds. This paper reports on a study that first explored the views and opinions of new professionals from several fields (using a qualitative approach), and then the views of their potential customers (a quantitative study informed by UTAUT2—the extended Unified Theory of Acceptance and Use of Technology). The results indicated that new professionals both create and co-create value with their customers, peers, and some existing, traditionally established professions. The results also indicated that the intended audience/customers of the new professional businesses had a positive perception of their long-term commercial sustainability. Customers’ intention to use the new professional services in the future were predicted mostly by the behavioral characteristics of hedonic motivation and habit. The research contributes by empirically investigating the value creation and co-creation processes in a context that is yet to attract academic interest. It proposes a value creation and co-creation framework that draws on the interactions of the main players. Full article
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25 pages, 3354 KiB  
Article
A Game-Based Learning Approach in Digital Design Course to Enhance Students’ Competency
by Chrysoula Velaora, Ioannis Dimos, Sofia Tsagiopoulou and Athanasios Kakarountas
Information 2022, 13(4), 177; https://doi.org/10.3390/info13040177 - 31 Mar 2022
Cited by 8 | Viewed by 5002
Abstract
Digital Design is a laboratory course, and the educator must focus on the students’ need to know why they study the theory and mainly on the transition from knowledge-based learning to competency-based learning. This study consists of five surveys that were conducted during [...] Read more.
Digital Design is a laboratory course, and the educator must focus on the students’ need to know why they study the theory and mainly on the transition from knowledge-based learning to competency-based learning. This study consists of five surveys that were conducted during 2017–2021. First, we evaluated students’ learning outcomes in order to define possible learning problems. According to the literature, gamification can have a positive impact on students’ motivation and learning outcomes. Therefore, we used ready-made digital games in order to evaluate students’ satisfaction and willingness toward their integration in the educational process. This process was repeated in the next academic year. The feedback we received from the previous surveys has helped us to adapt to the new approaches of teaching due to the current pandemic caused by COVID-19. We proposed an online holistic environment based on Keller’s (1987) ARCS model and Malone’s (1981) motivational model, which was applied in distance learning. Each student participated in a student-centered learning experience. He took an active role and was self-manager of his learning process. He was given the opportunity to develop capabilities and strategies through practice and engagement in higher-order cognitive activities, acquire self-learning skills, learn how to solve problems, and participate in teamwork. This study’s innovation is that students experienced a combination of learning approaches: (a) a virtual lab consisting of simulation-based activities, which allowed students to access new laboratory experiences, (b) a project-based digital game without a processor, which developed their motivation, creativity, and hands-on ability, as opposed to the other relevant studies that use ready-made games, and (c) asynchronous videos as feedback, which ensured the educator’s emotional support and social presence. Finally, this study developed research to evaluate the effectiveness of this online holistic environment and used a questionnaire, which was created based on Keller’s Instructional Materials Motivation Survey tool. The results showed that its integration in distance learning is probable to motivate students to learn and affect positively their attention, relevance, confidence, and satisfaction. Full article
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