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Volume 12
Issue 11
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Information, Volume 12, Issue 11 (November 2021) – 54 articles

Cover Story (view full-size image): Vehicular ad hoc networks (VANETs) are basic support for intelligent transportation systems, enabling communication among multiple road entities and fostering the development of new applications and services aimed at enhancing driving experience and increasing road safety. These are complex networks with highly demanding characteristics that pose great challenges to the implementation of security mechanisms, creating vulnerabilities that are easily exploitable by attackers. In this work, an intelligent hierarchical security framework is proposed. It makes use of machine learning algorithms to enhance attack detection and defines methods for secure communications among entities, ensuring strong authentication, privacy, and anonymity. View this paper
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23 pages, 2840 KiB  
Article
Multimatcher Model to Enhance Ontology Matching Using Background Knowledge
by Sohaib Al-Yadumi, Wei-Wei Goh, Ee-Xion Tan, Noor Zaman Jhanjhi and Patrice Boursier
Information 2021, 12(11), 487; https://doi.org/10.3390/info12110487 - 22 Nov 2021
Cited by 2 | Viewed by 2071
Abstract
Ontology matching is a rapidly emerging topic crucial for semantic web effort, data integration, and interoperability. Semantic heterogeneity is one of the most challenging aspects of ontology matching. Consequently, background knowledge (BK) resources are utilized to bridge the semantic gap between the ontologies. [...] Read more.
Ontology matching is a rapidly emerging topic crucial for semantic web effort, data integration, and interoperability. Semantic heterogeneity is one of the most challenging aspects of ontology matching. Consequently, background knowledge (BK) resources are utilized to bridge the semantic gap between the ontologies. Generic BK approaches use a single matcher to discover correspondences between entities from different ontologies. However, the Ontology Alignment Evaluation Initiative (OAEI) results show that not all matchers identify the same correct mappings. Moreover, none of the matchers can obtain good results across all matching tasks. This study proposes a novel BK multimatcher approach for improving ontology matching by effectively generating and combining mappings from biomedical ontologies. Aggregation strategies to create more effective mappings are discussed. Then, a matcher path confidence measure that helps select the most promising paths using the final mapping selection algorithm is proposed. The proposed model performance is tested using the Anatomy and Large Biomed tracks offered by the OAEI 2020. Results show that higher recall levels have been obtained. Moreover, the F-measure values achieved with our model are comparable with those obtained by the state of the art matchers. Full article
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13 pages, 1473 KiB  
Article
Analysis of Unsatisfying User Experiences and Unmet Psychological Needs for Virtual Reality Exergames Using Deep Learning Approach
by Xiaoyan Zhang, Qiang Yan, Simin Zhou, Linye Ma and Siran Wang
Information 2021, 12(11), 486; https://doi.org/10.3390/info12110486 - 22 Nov 2021
Cited by 3 | Viewed by 2323
Abstract
The number of consumers playing virtual reality games is booming. To speed up product iteration, the user experience team needs to collect and analyze unsatisfying experiences in time. In this paper, we aim to detect the unsatisfying experiences hidden in online reviews of [...] Read more.
The number of consumers playing virtual reality games is booming. To speed up product iteration, the user experience team needs to collect and analyze unsatisfying experiences in time. In this paper, we aim to detect the unsatisfying experiences hidden in online reviews of virtual reality exergames using a deep learning method and find out the unmet psychological needs of users based on self-determination theory. Convolutional neural networks for sentence classification (textCNN) are used in this study to classify online reviews with unsatisfying experiences. For comparison, we set eXtreme gradient boosting (XGBoost) with lexical features as the baseline of machine learning. Term frequency-inverse document frequency (TF-IDF) is used to extract keywords from every set of classified reviews. The micro-F1 score of textCNN classifier is 90.00, which is better than 82.69 of XGBoost. The top 10 keywords of every set of reviews reflect relevant topics of unmet psychological needs. This paper explores the potential problems causing unsatisfying experiences and unmet psychological needs in virtual reality exergames through text mining and makes a supplement for experimental studies about virtual reality exergames. Full article
(This article belongs to the Special Issue Text Mining: Classification, Clustering and Extraction Techniques)
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12 pages, 472 KiB  
Article
Predictive Maintenance for Switch Machine Based on Digital Twins
by Jia Yang, Yongkui Sun, Yuan Cao and Xiaoxi Hu
Information 2021, 12(11), 485; https://doi.org/10.3390/info12110485 - 22 Nov 2021
Cited by 15 | Viewed by 3855
Abstract
As a unique device of railway networks, the normal operation of switch machines involves railway safe and efficient operation. Predictive maintenance becomes the focus of the switch machine. Aiming at the low accuracy of the prediction state and the difficulty in state visualization, [...] Read more.
As a unique device of railway networks, the normal operation of switch machines involves railway safe and efficient operation. Predictive maintenance becomes the focus of the switch machine. Aiming at the low accuracy of the prediction state and the difficulty in state visualization, the paper proposes a predictive maintenance model for switch machines based on Digital Twins (DT). It constructs a DT model for the switch machine, which contains a behavior model and a rule model. The behavior model is a high-fidelity visual model. The rule model is a high-precision prediction model, which is combined with long short-term memory (LSTM) and autoregressive Integrated Moving Average model (ARIMA). Experiment results show that the model can be more intuitive with higher prediction accuracy and better applicability. The proposed DT approach is potentially practical, providing a promising idea for switching machines in predictive maintenance. Full article
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21 pages, 2307 KiB  
Article
Recent Advances in Dialogue Machine Translation
by Siyou Liu, Yuqi Sun and Longyue Wang
Information 2021, 12(11), 484; https://doi.org/10.3390/info12110484 - 22 Nov 2021
Cited by 6 | Viewed by 3512
Abstract
Recent years have seen a surge of interest in dialogue translation, which is a significant application task for machine translation (MT) technology. However, this has so far not been extensively explored due to its inherent characteristics including data limitation, discourse properties and personality [...] Read more.
Recent years have seen a surge of interest in dialogue translation, which is a significant application task for machine translation (MT) technology. However, this has so far not been extensively explored due to its inherent characteristics including data limitation, discourse properties and personality traits. In this article, we give the first comprehensive review of dialogue MT, including well-defined problems (e.g., 4 perspectives), collected resources (e.g., 5 language pairs and 4 sub-domains), representative approaches (e.g., architecture, discourse phenomena and personality) and useful applications (e.g., hotel-booking chat system). After systematical investigation, we also build a state-of-the-art dialogue NMT system by leveraging a breadth of established approaches such as novel architectures, popular pre-training and advanced techniques. Encouragingly, we push the state-of-the-art performance up to 62.7 BLEU points on a commonly-used benchmark by using mBART pre-training. We hope that this survey paper could significantly promote the research in dialogue MT. Full article
(This article belongs to the Special Issue Frontiers in Machine Translation)
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30 pages, 8350 KiB  
Article
An Analytical and Numerical Detour for the Riemann Hypothesis
by Michel Riguidel
Information 2021, 12(11), 483; https://doi.org/10.3390/info12110483 - 21 Nov 2021
Viewed by 1986
Abstract
From the functional equation F(s)=F(1−s) of Riemann’s zeta function, this article gives new insight into Hadamard’s product formula. The function [...] Read more.
From the functional equation F(s)=F(1−s) of Riemann’s zeta function, this article gives new insight into Hadamard’s product formula. The function S1(s)=d(lnF(s))/ds and its family of associated Sm functions, expressed as a sum of rational fractions, are interpreted as meromorphic functions whose poles are the poles and zeros of the F function. This family is a mathematical and numerical tool which makes it possible to estimate the value F(s) of the function at a point s=x+iy=x˙+½+iy in the critical strip S from a point 𝓈=½+iy on the critical line ℒ.Generating estimates Sm∗(s) of Sm(s) at a given point requires a large number of adjacent zeros, due to the slow convergence of the series. The process allows a numerical approach of the Riemann hypothesis (RH). The method can be extended to other meromorphic functions, in the neighborhood of isolated zeros, inspired by the Weierstraß canonical form. A final and brief comparison is made with the ζ and F functions over finite fields. Full article
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14 pages, 687 KiB  
Article
Raising Awareness about Cloud Security in Industry through a Board Game
by Tiange Zhao, Tiago Gasiba, Ulrike Lechner and Maria Pinto-Albuquerque
Information 2021, 12(11), 482; https://doi.org/10.3390/info12110482 - 19 Nov 2021
Cited by 9 | Viewed by 2749
Abstract
Today, many products and solutions are provided on the cloud; however, the amount and financial losses due to cloud security incidents illustrate the critical need to do more to protect cloud assets adequately. A gap lies in transferring what cloud and security standards [...] Read more.
Today, many products and solutions are provided on the cloud; however, the amount and financial losses due to cloud security incidents illustrate the critical need to do more to protect cloud assets adequately. A gap lies in transferring what cloud and security standards recommend and require to industry practitioners working in the front line. It is of paramount importance to raise awareness about cloud security of these industrial practitioners. Under the guidance of design science paradigm, we introduce a serious game to help participants understand the inherent risks, understand the different roles, and encourage proactive defensive thinking in defending cloud assets. In our game, we designed and implemented an automated evaluator as a novel element. We invite the players to build defense plans and attack plans for which the evaluator calculates success likelihoods. The primary target group is industry practitioners, whereas people with limited background knowledge about cloud security can also participate in and benefit from the game. We design the game and organize several trial runs in an industrial setting. Observations of the trial runs and collected feedback indicate that the game ideas and logic are useful and provide help in raising awareness of cloud security in industry. Our preliminary results share insight into the design of the serious game and are discussed in this paper. Full article
(This article belongs to the Special Issue Future Trends in Computer Programming Education)
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2 pages, 170 KiB  
Editorial
Special Issue on Emerging Trends and Challenges in Supervised Learning Tasks
by Barbara Pes
Information 2021, 12(11), 481; https://doi.org/10.3390/info12110481 - 19 Nov 2021
Viewed by 1756
Abstract
With the massive growth of data-intensive applications, the machine learning field has gained widespread popularity [...] Full article
(This article belongs to the Special Issue Emerging Trends and Challenges in Supervised Learning Tasks)
36 pages, 5333 KiB  
Review
Personalized Advertising Computational Techniques: A Systematic Literature Review, Findings, and a Design Framework
by Iosif Viktoratos and Athanasios Tsadiras
Information 2021, 12(11), 480; https://doi.org/10.3390/info12110480 - 19 Nov 2021
Cited by 5 | Viewed by 7211
Abstract
This work conducts a systematic literature review about the domain of personalized advertisement, and more specifically, about the techniques that are used for this purpose. State-of-the-art publications and techniques are presented in detail, and the relationship of this domain with other related domains [...] Read more.
This work conducts a systematic literature review about the domain of personalized advertisement, and more specifically, about the techniques that are used for this purpose. State-of-the-art publications and techniques are presented in detail, and the relationship of this domain with other related domains such as artificial intelligence (AI), semantic web, etc., is investigated. Important issues such as (a) business data utilization in personalized advertisement models, (b) the cold start problem in the domain, (c) advertisement visualization issues, (d) psychological factors in the personalization models, (e) the lack of rich datasets, and (f) user privacy are highlighted and are pinpointed to help and inspire researchers for future work. Finally, a design framework for personalized advertisement systems has been designed based on these findings. Full article
(This article belongs to the Section Review)
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23 pages, 718 KiB  
Article
The Use of ICT for Communication between Teachers and Students in the Context of Higher Education Institutions
by João Batista, Helena Santos and Rui Pedro Marques
Information 2021, 12(11), 479; https://doi.org/10.3390/info12110479 - 19 Nov 2021
Cited by 5 | Viewed by 6566
Abstract
Recently, the communication paradigm has been changing in society in the higher education context because of the ease of access to the Internet and the high number of mobile devices. Thus, universities have increased their interest in accepting different and sophisticated communication technologies [...] Read more.
Recently, the communication paradigm has been changing in society in the higher education context because of the ease of access to the Internet and the high number of mobile devices. Thus, universities have increased their interest in accepting different and sophisticated communication technologies to improve student participation in the educational process. This study aimed to assess how students and teachers use communication technologies to communicate with each other and what their expectations, satisfaction, and attitudes regarding the results of this use are. An analysis model was used in a case study at the University of Aveiro to support the study. Data were obtained through an online questionnaire, which collected 570 responses from students and 172 responses from teachers. These data were processed through descriptive statistics techniques and inference tests (t-tests). The primary outcomes are that publishing and sharing technologies and electronic mail are the most commonly used communication technologies by students and teachers, suggesting that their use will not decline soon. However, other communication technologies were also revealed to be widely used and accepted, with excellent levels of confirmation of expectation. Full article
(This article belongs to the Section Information Applications)
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11 pages, 230 KiB  
Article
Risk Factors When Implementing ERP Systems in Small Companies
by Ann Svensson and Alexander Thoss
Information 2021, 12(11), 478; https://doi.org/10.3390/info12110478 - 19 Nov 2021
Cited by 8 | Viewed by 8467
Abstract
Implementation of enterprise resource planning (ERP) systems often aims to improve the companies’ processes in order to gain competitive advantage on the market. Especially, small companies need to integrate systems with suppliers and customers; hence, ERP systems often become a requirement. ERP system [...] Read more.
Implementation of enterprise resource planning (ERP) systems often aims to improve the companies’ processes in order to gain competitive advantage on the market. Especially, small companies need to integrate systems with suppliers and customers; hence, ERP systems often become a requirement. ERP system implementation processes in small enterprises contain several risk factors. Research has concluded that ERP implementation projects fail to a relatively high degree. Small companies are found to be constrained by limited resources, limited IS (information systems) knowledge and lack of IT expertise in ERP implementation. There are relatively few empirical research studies on implementing ERP systems in small enterprises and there is a large gap in research that could guide managers of small companies. This paper is based on a case study of three small enterprises that are planning to implement ERP systems that support their business processes. The aim of the paper is to identify the risk factors that can arise when implementing ERP systems in small enterprises. The analysis shows that an ERP system is a good solution to avoid using many different, separate systems in parallel. However, the study shows that it is challenging to integrate all systems used by suppliers and customers. An ERP system can include all information in one system and all information can also easily be accessed within that system. However, the implementation could be a demanding process as it requires engagement from all involved people, especially the managers of the companies. Full article
(This article belongs to the Special Issue Information Technology: New Generations (ITNG 2020 & 2021))
17 pages, 647 KiB  
Article
The Impact of Social Media Activities on Brand Equity
by Ra’ed Masa’deh, Shafig AL-Haddad, Dana Al Abed, Hadeel Khalil, Lina AlMomani and Taghreed Khirfan
Information 2021, 12(11), 477; https://doi.org/10.3390/info12110477 - 18 Nov 2021
Cited by 18 | Viewed by 17445
Abstract
This study aims to investigate the impact of Social Media Activities on brand equity (brand awareness and brand image). A cross-sectional quantitative study has been conducted using a validated questionnaire distributed to a convenience sample of 362 participants who used one or more [...] Read more.
This study aims to investigate the impact of Social Media Activities on brand equity (brand awareness and brand image). A cross-sectional quantitative study has been conducted using a validated questionnaire distributed to a convenience sample of 362 participants who used one or more forms of an Airline’s social media. Multiple Regression analysis was performed using SPSS version 20 to test the hypotheses. Results revealed a significant impact of Social Media Activities as a whole on brand equity. It was found that entertainment, customization, interaction and EWOM significantly affected the brand image, while customization, trendiness, interaction and EWOM significantly affected brand awareness. This study is one of few to examine the impact of social media activities on brand equity towards Airlines in Middle Eastern countries. The study provided several theoretical and practical implications that can benefit airline managers in their marketing efforts using various social media activities. Full article
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21 pages, 4827 KiB  
Article
Predicting Student Dropout in Self-Paced MOOC Course Using Random Forest Model
by Sheran Dass, Kevin Gary and James Cunningham
Information 2021, 12(11), 476; https://doi.org/10.3390/info12110476 - 17 Nov 2021
Cited by 36 | Viewed by 5411
Abstract
A significant problem in Massive Open Online Courses (MOOCs) is the high rate of student dropout in these courses. An effective student dropout prediction model of MOOC courses can identify the factors responsible and provide insight on how to initiate interventions to increase [...] Read more.
A significant problem in Massive Open Online Courses (MOOCs) is the high rate of student dropout in these courses. An effective student dropout prediction model of MOOC courses can identify the factors responsible and provide insight on how to initiate interventions to increase student success in a MOOC. Different features and various approaches are available for the prediction of student dropout in MOOC courses. In this paper, the data derived from a self-paced math course, College Algebra and Problem Solving, offered on the MOOC platform Open edX partnering with Arizona State University (ASU) from 2016 to 2020 is considered. This paper presents a model to predict the dropout of students from a MOOC course given a set of features engineered from student daily learning progress. The Random Forest Model technique in Machine Learning (ML) is used in the prediction and is evaluated using validation metrics including accuracy, precision, recall, F1-score, Area Under the Curve (AUC), and Receiver Operating Characteristic (ROC) curve. The model developed can predict the dropout or continuation of students on any given day in the MOOC course with an accuracy of 87.5%, AUC of 94.5%, precision of 88%, recall of 87.5%, and F1-score of 87.5%, respectively. The contributing features and interactions were explained using Shapely values for the prediction of the model. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Education)
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15 pages, 932 KiB  
Article
The Use of Information and Communication Technology (ICT) in the Implementation of Instructional Supervision and Its Effect on Teachers’ Instructional Process Quality
by Bambang Budi Wiyono, Agus Wedi, Saida Ulfa and Arda Purnama Putra
Information 2021, 12(11), 475; https://doi.org/10.3390/info12110475 - 16 Nov 2021
Cited by 7 | Viewed by 6120
Abstract
This study aimed to explore communication techniques based on the information and communication technology (ICT) used in the implementation of instructional supervision to determine their effect on the teacher’s learning process and find effective techniques to improve the quality of the teacher’s learning [...] Read more.
This study aimed to explore communication techniques based on the information and communication technology (ICT) used in the implementation of instructional supervision to determine their effect on the teacher’s learning process and find effective techniques to improve the quality of the teacher’s learning process. This research was conducted in Blitar City with a sample of 60 teachers through a random sampling technique. The data collection technique used a rating scale, checklist, and open-form questionnaire. Descriptive statistics were used to describe the data, while the Pearson product-moment correlation techniques and multiple regression were used to test the research hypotheses. The results show that the most widely used ICT-based communication techniques are WhatsApp, Google Meet, Zoom, Skype, and Google Forms. These are followed by email, video-recording, and audio-recording techniques. The use of ICT is still rare. There is a significant relationship between the use of ICT in instructional supervision and the quality of the teacher’s teaching-learning process, except when using telephones and televisions. ICT techniques are most commonly used for synchronous communication, followed by use for sharing information, and recording activities. The use of ICT in instructional supervision simultaneously affects the teacher’s instructional process. Full article
(This article belongs to the Special Issue ICCCI 2020&2021: Advances in Baseband Signal Processing, Circuit Designs, and Communications)
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19 pages, 8460 KiB  
Article
DBA_SSD: A Novel End-to-End Object Detection Algorithm Applied to Plant Disease Detection
by Jun Wang, Liya Yu, Jing Yang and Hao Dong
Information 2021, 12(11), 474; https://doi.org/10.3390/info12110474 - 16 Nov 2021
Cited by 33 | Viewed by 3899
Abstract
In response to the difficulty of plant leaf disease detection and classification, this study proposes a novel plant leaf disease detection method called deep block attention SSD (DBA_SSD) for disease identification and disease degree classification of plant leaves. We propose three plant leaf [...] Read more.
In response to the difficulty of plant leaf disease detection and classification, this study proposes a novel plant leaf disease detection method called deep block attention SSD (DBA_SSD) for disease identification and disease degree classification of plant leaves. We propose three plant leaf detection methods, namely, squeeze-and-excitation SSD (Se_SSD), deep block SSD (DB_SSD), and DBA_SSD. Se_SSD fuses SSD feature extraction network and attention mechanism channel, DB_SSD improves VGG feature extraction network, and DBA_SSD fuses the improved VGG network and channel attention mechanism. To reduce the training time and accelerate the training process, the convolutional layers trained in the Image Net image dataset by the VGG model are migrated to this model, whereas the collected plant leaves disease image dataset is randomly divided into training set, validation set, and test set in the ratio of 8:1:1. We chose the PlantVillage dataset after careful consideration because it contains images related to the domain of interest. This dataset consists of images of 14 plants, including images of apples, tomatoes, strawberries, peppers, and potatoes, as well as the leaves of other plants. In addition, data enhancement methods, such as histogram equalization and horizontal flip were used to expand the image data. The performance of the three improved algorithms is compared and analyzed in the same environment and with the classical target detection algorithms YOLOv4, YOLOv3, Faster RCNN, and YOLOv4 tiny. Experiments show that DBA_SSD outperforms the two other improved algorithms, and its performance in comparative analysis is superior to other target detection algorithms. Full article
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27 pages, 1011 KiB  
Article
Help Me Learn! Architecture and Strategies to Combine Recommendations and Active Learning in Manufacturing
by Patrik Zajec, Jože M. Rožanec, Elena Trajkova, Inna Novalija, Klemen Kenda, Blaž Fortuna and Dunja Mladenić
Information 2021, 12(11), 473; https://doi.org/10.3390/info12110473 - 16 Nov 2021
Cited by 4 | Viewed by 3280
Abstract
This research work describes an architecture for building a system that guides a user from a forecast generated by a machine learning model through a sequence of decision-making steps. The system is demonstrated in a manufacturing demand forecasting use case and can be [...] Read more.
This research work describes an architecture for building a system that guides a user from a forecast generated by a machine learning model through a sequence of decision-making steps. The system is demonstrated in a manufacturing demand forecasting use case and can be extended to other domains. In addition, the system provides the means for knowledge acquisition by gathering data from users. Finally, it implements an active learning component and compares multiple strategies to recommend media news to the user. We compare such strategies through a set of experiments to understand how they balance learning and provide accurate media news recommendations to the user. The media news aims to provide additional context to demand forecasts and enhance judgment on decision-making. Full article
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13 pages, 621 KiB  
Article
Revolutions Take Time
by Peter Wittenburg and George Strawn
Information 2021, 12(11), 472; https://doi.org/10.3390/info12110472 - 16 Nov 2021
Cited by 2 | Viewed by 2108
Abstract
The 2018 paper titled “Common Patterns in Revolutionary Infrastructures and Data” has been cited frequently, since we compared the current discussions about research data management with the developments of large infrastructures in the past believing, similar to philosophers such as Luciano Floridi, that [...] Read more.
The 2018 paper titled “Common Patterns in Revolutionary Infrastructures and Data” has been cited frequently, since we compared the current discussions about research data management with the developments of large infrastructures in the past believing, similar to philosophers such as Luciano Floridi, that the creation of an interoperable data domain will also be a revolutionary step. We identified the FAIR principles and the FAIR Digital Objects as nuclei for achieving the necessary convergence without which such new infrastructures will not take up. In this follow-up paper, we are elaborating on some factors that indicate that it will still take much time until breakthroughs will be achieved which is mainly devoted to sociological and political reasons. Therefore, it is important to describe visions such as FDO as self-standing entities, the easy plug-in concept, and the built-in security more explicitly to give a long-range perspective and convince policymakers and decision-makers. We also looked at major funding programs which all follow different approaches and do not define a converging core yet. This can be seen as an indication that these funding programs have huge potentials and increase awareness about data management aspects, but that we are far from converging agreements which we finally will need to create a globally integrated data space in the future. Finally, we discuss the roles of some major stakeholders who are all relevant in the process of agreement finding. Most of them are bound by short-term project cycles and funding constraints, not giving them sufficient space to work on long-term convergence concepts and take risks. The great opportunity to get funds for projects improving approaches and technology with the inherent danger of promising too much and the need for continuous reporting and producing visible results after comparably short periods is like a vicious cycle without a possibility to break out. We can recall that coming to the Internet with TCP/IP as a convergence standard was dependent on years of DARPA funding. Building large revolutionary infrastructures seems to be dependent on decision-makers that dare to think strategically and test out promising concepts at a larger scale. Full article
(This article belongs to the Special Issue Data and Metadata Management with Semantic Technologies)
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14 pages, 2978 KiB  
Article
Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT
by You-Zhen Feng, Sidong Liu, Zhong-Yuan Cheng, Juan C. Quiroz, Dana Rezazadegan, Ping-Kang Chen, Qi-Ting Lin, Long Qian, Xiao-Fang Liu, Shlomo Berkovsky, Enrico Coiera, Lei Song, Xiao-Ming Qiu and Xiang-Ran Cai
Information 2021, 12(11), 471; https://doi.org/10.3390/info12110471 - 15 Nov 2021
Cited by 10 | Viewed by 3050
Abstract
Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic [...] Read more.
Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from individual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses. Full article
(This article belongs to the Special Issue Advances in AI for Health and Medical Applications)
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17 pages, 3236 KiB  
Article
Usage and Temporal Patterns of Public Bicycle Systems: Comparison among Points of Interest
by Xingchen Yan, Liangpeng Gao, Jun Chen and Xiaofei Ye
Information 2021, 12(11), 470; https://doi.org/10.3390/info12110470 - 15 Nov 2021
Cited by 2 | Viewed by 1940
Abstract
The public bicycle system is an important component of “mobility as a service” and has become increasingly popular in recent years. To provide a better understanding of the station activity and driving mechanisms of public bicycle systems, the study mainly compares the usage [...] Read more.
The public bicycle system is an important component of “mobility as a service” and has become increasingly popular in recent years. To provide a better understanding of the station activity and driving mechanisms of public bicycle systems, the study mainly compares the usage and temporal characteristics of public bicycles in the vicinity of the most common commuting-related points of interest and land use. It applies the peak hour factor, distribution fitting, and K-means clustering analysis on station-based data and performs the public bicycles usage and operation comparison among different points of interest and land use. The following results are acquired: (1) the demand type for universities and hospitals in peaks is return-oriented when that of middle schools is hire-oriented; (2) bike hire and return at metro stations and hospitals are frequent, while only the rental at malls is; (3) compared to middle schools and subway stations with the shortest bike usage duration, malls have the longest duration, valued at 18.08 min; and (4) medical and transportation land, with the most obvious morning return peak and the most concentrated usage in a whole day, respectively, both present a lag relation between bike rental and return. In rental-return similarity, the commercial and office lands present the highest level. Full article
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14 pages, 4056 KiB  
Article
Partial Fractional Fourier Transform (PFrFT)-MIMO-OFDM for Known Underwater Acoustic Communication Channels
by Yixin Chen, Carmine Clemente and John J. Soraghan
Information 2021, 12(11), 469; https://doi.org/10.3390/info12110469 - 12 Nov 2021
Cited by 1 | Viewed by 1948
Abstract
Communication over doubly selective channels (both time and frequency selective) suffers from significant intercarrier interference (ICI). This problem is severe in underwater acoustic communications. In this paper, a novel partial fractional (PFrFT)-MIMO-OFDM system is proposed and implemented to further mitigate ICI. A new [...] Read more.
Communication over doubly selective channels (both time and frequency selective) suffers from significant intercarrier interference (ICI). This problem is severe in underwater acoustic communications. In this paper, a novel partial fractional (PFrFT)-MIMO-OFDM system is proposed and implemented to further mitigate ICI. A new iterative band minimum mean square error (BMMSE) weight combining based on LDLH factorization is used in a scenario of perfect knowledge of channel information. The proposed method is extended from SISO-OFDM configuration to MIMO-OFDM. Simulation results demonstrate that the proposed PFrFT-LDLH outperforms the other methods in the SISO-OFDM scenario and that its performance can be improved in MIMO-OFDM scenarios. Full article
(This article belongs to the Special Issue Channel Estimation and Detection for Large-Scale MIMO Systems)
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19 pages, 1570 KiB  
Article
Analyzing the Behavior and Financial Status of Soccer Fans from a Mobile Phone Network Perspective: Euro 2016, a Case Study
by Gergő Pintér and Imre Felde
Information 2021, 12(11), 468; https://doi.org/10.3390/info12110468 - 12 Nov 2021
Cited by 6 | Viewed by 2292
Abstract
In this study, Call Detail Records (CDRs) covering Budapest for the month of June in 2016 were analyzed. During this observation period, the 2016 UEFA European Football Championship took place, which significantly affected the habit of the residents despite the fact that not [...] Read more.
In this study, Call Detail Records (CDRs) covering Budapest for the month of June in 2016 were analyzed. During this observation period, the 2016 UEFA European Football Championship took place, which significantly affected the habit of the residents despite the fact that not a single match was played in the city. We evaluated the fans’ behavior in Budapest during and after the Hungarian matches and found that the mobile phone network activity reflected the football fans’ behavior, demonstrating the potential of the use of mobile phone network data in a social sensing system. The Call Detail Records were enriched with mobile phone properties and used to analyze the subscribers’ devices. Applying the device information (Type Allocation Code) obtained from the activity records, the Subscriber Identity Modules (SIM), which do not operate in cell phones, were omitted from mobility analyses, allowing us to focus on the behavior of people. Mobile phone price was proposed and evaluated as a socioeconomic indicator and the correlation between the phone price and the mobility customs was found. We also found that, besides the cell phone price, the subscriber age and subscription type also had effects on users’ mobility. On the other hand, these factors did not seem to affect their interest in football. Full article
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16 pages, 5838 KiB  
Article
Research on Building DSM Fusion Method Based on Adaptive Spline and Target Characteristic Guidance
by Jinming Liu, Hao Chen and Shuting Yang
Information 2021, 12(11), 467; https://doi.org/10.3390/info12110467 - 10 Nov 2021
Cited by 2 | Viewed by 1610 | Correction
Abstract
In order to adapt to the actual scene of a stereo satellite observing the same area sequentially and improve the accuracy of the target-oriented 3D reconstruction, this paper proposed a building DSM fusion update method based on adaptive splines and target characteristic guidance. [...] Read more.
In order to adapt to the actual scene of a stereo satellite observing the same area sequentially and improve the accuracy of the target-oriented 3D reconstruction, this paper proposed a building DSM fusion update method based on adaptive splines and target characteristic guidance. This method analyzed the target characteristics of surface building targets to explore their intrinsic geometric structure information, established a nonlinear fusion method guided by the target characteristics to achieve the effective fusion of multiple DSMs on the basis of maintaining the target structural characteristics, and supported the online updating of DSM to ensure the needs of practical engineering applications. This paper presented a DSM fusion method for surface building targets and finally conducted DSM fusion experiments using typical urban area images of different scenes. The experimental results showed that the proposed method can effectively constrain and improve the DSM of buildings, and the integrity of the overall construction of the target 3D model structure was significantly improved, indicating that this paper provides an effective and efficient DSM constraint method for buildings. Full article
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11 pages, 2095 KiB  
Article
Tortuosity Index Calculations in Retinal Images: Some Criticalities Arising from Commonly Used Approaches
by Francesco Martelli and Claudia Giacomozzi
Information 2021, 12(11), 466; https://doi.org/10.3390/info12110466 - 10 Nov 2021
Cited by 5 | Viewed by 11418
Abstract
A growing body of research in retinal imaging is recently considering vascular tortuosity measures or indexes, with definitions and methods mostly derived from cardiovascular research. However, retinal microvasculature has its own peculiarities that must be considered in order to produce reliable measurements. This [...] Read more.
A growing body of research in retinal imaging is recently considering vascular tortuosity measures or indexes, with definitions and methods mostly derived from cardiovascular research. However, retinal microvasculature has its own peculiarities that must be considered in order to produce reliable measurements. This study analyzed and compared various derived metrics (e.g., TI, TI_avg, TI*CV) across four existing computational workflows. Specifically, the implementation of the models on two critical OCT images highlighted main pitfalls of the methods, which may fail in reliably differentiating a highly tortuous image from a normal one. A tentative, encouraging approach to mitigate the issue on the same OCT exemplificative images is described in the paper, based on the suggested index TI*CV. Full article
(This article belongs to the Special Issue Biosignal and Medical Image Processing)
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32 pages, 3880 KiB  
Review
Data Ownership: A Survey
by Jad Asswad and Jorge Marx Gómez
Information 2021, 12(11), 465; https://doi.org/10.3390/info12110465 - 10 Nov 2021
Cited by 17 | Viewed by 9183
Abstract
The importance of data is increasing along its inflation in our world today. In the big data era, data is becoming a main source for innovation, knowledge and insight, as well as a competitive and financial advantage in the race of information procurement. [...] Read more.
The importance of data is increasing along its inflation in our world today. In the big data era, data is becoming a main source for innovation, knowledge and insight, as well as a competitive and financial advantage in the race of information procurement. This interest in acquiring and exploiting data, in addition to the existing concerns regarding the privacy and security of information, raises the question of who should own the data and how the ownership of data can be preserved. This paper discusses and analyses the concept of data ownership and provides an overview on the subject from different point of views. It surveys also the state-of-the-art of data ownership in health, transportation, industry, energy and smart cities sectors and outlines lessons learned with an extended definition of data ownership that may pave the way for future research and work in this area. Full article
(This article belongs to the Section Review)
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17 pages, 3419 KiB  
Article
Effect of Personality Traits on Banner Advertisement Recognition
by Stefanos Balaskas and Maria Rigou
Information 2021, 12(11), 464; https://doi.org/10.3390/info12110464 - 10 Nov 2021
Cited by 1 | Viewed by 3077
Abstract
This article investigates the effect of personality traits on the attitude of web users towards online advertising. Utilizing and analyzing personality traits along with possible correlations between these traits and their influence on consumers’ buying behavior is crucial not only in research studies; [...] Read more.
This article investigates the effect of personality traits on the attitude of web users towards online advertising. Utilizing and analyzing personality traits along with possible correlations between these traits and their influence on consumers’ buying behavior is crucial not only in research studies; this also holds for commercial implementations, as it allows businesses to set up and run sophisticated and strategic campaign designs in the field of digital marketing. This article starts with a literature review on advertisement recall and personality traits, which is followed by the procedure and processes undertaken to conduct the experiment, construct the online store, and design and place the advertisements. Collected data from the personality questionnaire and the two experiment questionnaires (pre and post-test) are presented using descriptive statistics, and data collected from the eye-tracking are analyzed using visual behavior assessment metrics. The results show that personality traits and especially honesty–humility can prove to be a predictive force for some important aspects of banner advertisement recognizability. Full article
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36 pages, 1477 KiB  
Article
RADAR: Resilient Application for Dependable Aided Reporting
by Antonia Azzini, Nicola Cortesi and Giuseppe Psaila
Information 2021, 12(11), 463; https://doi.org/10.3390/info12110463 - 9 Nov 2021
Cited by 2 | Viewed by 5476
Abstract
Many organizations must produce many reports for various reasons. Although this activity could appear simple to carry out, this fact is not at all true: indeed, generating reports requires the collection of possibly large and heterogeneous data sets. Furthermore, different professional figures are [...] Read more.
Many organizations must produce many reports for various reasons. Although this activity could appear simple to carry out, this fact is not at all true: indeed, generating reports requires the collection of possibly large and heterogeneous data sets. Furthermore, different professional figures are involved in the process, possibly with different skills (database technicians, domain experts, employees): the lack of common knowledge and of a unifying framework significantly obstructs the effective and efficient definition and continuous generation of reports. This paper presents a novel framework named RADAR, which is the acronym for “Resilient Application for Dependable Aided Reporting”: the framework has been devised to be a ”bridge” between data and employees in charge of generating reports. Specifically, it builds a common knowledge base in which database administrators and domain experts describe their knowledge about the application domain and the gathered data; this knowledge can be browsed by employees to find out the relevant data to aggregate and insert into reports, while designing report layouts; the framework assists the overall process from data definition to report generation. The paper presents the application scenario and the vision by means of a running example, defines the data model and presents the architecture of the framework. Full article
(This article belongs to the Special Issue Semantic Web and Information Systems)
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12 pages, 528 KiB  
Article
Profiling Attack against RSA Key Generation Based on a Euclidean Algorithm
by Sadiel de la Fe, Han-Byeol Park, Bo-Yeon Sim, Dong-Guk Han and Carles Ferrer
Information 2021, 12(11), 462; https://doi.org/10.3390/info12110462 - 9 Nov 2021
Cited by 3 | Viewed by 2975
Abstract
A profiling attack is a powerful variant among the noninvasive side channel attacks. In this work, we target RSA key generation relying on the binary version of the extended Euclidean algorithm for modular inverse and GCD computations. To date, this algorithm has only [...] Read more.
A profiling attack is a powerful variant among the noninvasive side channel attacks. In this work, we target RSA key generation relying on the binary version of the extended Euclidean algorithm for modular inverse and GCD computations. To date, this algorithm has only been exploited by simple power analysis; therefore, the countermeasures described in the literature are focused on mitigating only this kind of attack. We demonstrate that one of those countermeasures is not effective in preventing profiling attacks. The feasibility of our approach relies on the extraction of several leakage vectors from a single power trace. Moreover, because there are known relationships between the secrets and the public modulo in RSA, the uncertainty in some of the guessed secrets can be reduced by simple tests. This increases the effectiveness of the proposed attack. Full article
(This article belongs to the Special Issue Side Channel Attacks and Defenses on Cryptography)
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13 pages, 4035 KiB  
Article
Automation of Basketball Match Data Management
by Łukasz Chomątek and Kinga Sierakowska
Information 2021, 12(11), 461; https://doi.org/10.3390/info12110461 - 8 Nov 2021
Cited by 1 | Viewed by 2384
Abstract
Despite the fact that sport plays a substantial role in people’s lives, funding varies significantly from one discipline to another. For example, in Poland, women’s basketball in the lower divisions, is primarily developing thanks to enthusiasts. The aim of the work was to [...] Read more.
Despite the fact that sport plays a substantial role in people’s lives, funding varies significantly from one discipline to another. For example, in Poland, women’s basketball in the lower divisions, is primarily developing thanks to enthusiasts. The aim of the work was to design and implement a system for analyzing match protocols containing data about the match. Particular attention was devoted to the course of the game, i.e., the order of scoring points. This type of data is not typically stored on the official websites of basketball associations but is significant from the point of view of coaches. The obtained data can be utilized to analyze the team’s game during the season, the quality of players, etc. In terms of obtaining data from match protocols, a dedicated algorithm for identifying the table was used, while a neural network was utilized to recognize the numbers (with 70% accuracy). The conducted research has shown the proposed system is well suited for data acquisition based on match protocols what implies the possibility of increasing the availability of data on the games. This will allow the development of this sport discipline. Obtained conclusions can be generalized to other disciplines, where the games are recorded in paper form. 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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16 pages, 4080 KiB  
Article
Asset Management Method of Industrial IoT Systems for Cyber-Security Countermeasures
by Noritaka Matsumoto, Junya Fujita, Hiromichi Endoh, Tsutomu Yamada, Kenji Sawada and Osamu Kaneko
Information 2021, 12(11), 460; https://doi.org/10.3390/info12110460 - 8 Nov 2021
Cited by 5 | Viewed by 4057
Abstract
Cyber-security countermeasures are important for IIoT (industrial Internet of things) systems in which IT (information technology) and OT (operational technology) are integrated. The appropriate asset management is the key to creating strong security systems to protect from various cyber threats. However, the timely [...] Read more.
Cyber-security countermeasures are important for IIoT (industrial Internet of things) systems in which IT (information technology) and OT (operational technology) are integrated. The appropriate asset management is the key to creating strong security systems to protect from various cyber threats. However, the timely and coherent asset management methods used for conventional IT systems are difficult to be implemented for IIoT systems. This is because these systems are composed of various network protocols, various devices, and open technologies. Besides, it is necessary to guarantee reliable and real-time control and save CPU and memory usage for legacy OT devices. In this study, therefore, (1) we model various asset configurations for IIoT systems and design a data structure based on SCAP (Security Content Automation Protocol). (2) We design the functions to automatically acquire the detailed information from edge devices by “asset configuration management agent”, which ensures a low processing load. (3) We implement the proposed asset management system to real edge devices and evaluate the functions. Our contribution is to automate the asset management method that is valid for the cyber security countermeasures in the IIoT systems. Full article
(This article belongs to the Special Issue Recent Advances in IoT and Cyber/Physical Security)
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16 pages, 1327 KiB  
Article
Exploring the Impact of COVID-19 on Social Life by Deep Learning
by Jose Antonio Jijon-Vorbeck and Isabel Segura-Bedmar
Information 2021, 12(11), 459; https://doi.org/10.3390/info12110459 - 5 Nov 2021
Cited by 3 | Viewed by 2900
Abstract
Due to the globalisation of the COVID-19 pandemic, and the expansion of social media as the main source of information for many people, there have been a great variety of different reactions surrounding the topic. The World Health Organization (WHO) announced in December [...] Read more.
Due to the globalisation of the COVID-19 pandemic, and the expansion of social media as the main source of information for many people, there have been a great variety of different reactions surrounding the topic. The World Health Organization (WHO) announced in December 2020 that they were currently fighting an “infodemic” in the same way as they were fighting the pandemic. An “infodemic” relates to the spread of information that is not controlled or filtered, and can have a negative impact on society. If not managed properly, an aggressive or negative tweet can be very harmful and misleading among its recipients. Therefore, authorities at WHO have called for action and asked the academic and scientific community to develop tools for managing the infodemic by the use of digital technologies and data science. The goal of this study is to develop and apply natural language processing models using deep learning to classify a collection of tweets that refer to the COVID-19 pandemic. Several simpler and widely used models are applied first and serve as a benchmark for deep learning methods, such as Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT). The results of the experiments show that the deep learning models outperform the traditional machine learning algorithms. The best approach is the BERT-based model. Full article
(This article belongs to the Special Issue Sentiment Analysis and Affective Computing)
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12 pages, 626 KiB  
Article
Measuring Discrimination against Older People Applying the Fraboni Scale of Ageism
by Ágnes Hofmeister-Tóth, Ágnes Neulinger and János Debreceni
Information 2021, 12(11), 458; https://doi.org/10.3390/info12110458 - 5 Nov 2021
Cited by 3 | Viewed by 4123
Abstract
The progressive aging of developed societies, caused by profound demographic changes, brings with it the necessity of confronting the subject of discrimination against older people. In the last 50 years, many scales of ageism have been developed to measure beliefs and attitudes towards [...] Read more.
The progressive aging of developed societies, caused by profound demographic changes, brings with it the necessity of confronting the subject of discrimination against older people. In the last 50 years, many scales of ageism have been developed to measure beliefs and attitudes towards older adults. The purpose of our study was to adapt the full Fraboni Scale of Ageism (FSA) to Hungarian language and assess its reliability, validity, and psychometric properties. The sample of the study was representative of the Hungarian population, and the data collection took place online. In our study, we compare the dimensions of the scale with other international studies and present the attitudes and biases of the Hungarian population against the older people. The results of the study indicate that attitudes toward older people are more positive among women, older people, and people living in villages. In this study, we concluded that the Hungarian version of the Fraboni Scale of Ageism is a suitable instrument for both measuring the extent of ageism in the Hungarian population and contributing to further testing the international reliability, validity, and psychometric properties of the Fraboni Scale of Ageism. Full article
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