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Information, Volume 14, Issue 2 (February 2023) – 83 articles

Cover Story (view full-size image): The manuscript proposes a novel system that analyzes visual data on social media during extreme events to improve situational awareness and support emergency management. By considering the event type and GPS coordinates of the affected area, the system selects relevant user-generated image posts and applies a Single-Shot Multibox Detector (SSD) network to refine the selection. Advanced image processing is then used to verify the correlation between images and the affected area. The system has been extensively tested in various emergency situations to assess its performance and reliability. It has the potential to be a valuable tool for emergency managers to gather information quickly and effectively. View this paper
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14 pages, 1211 KiB  
Article
Co-Channel Interference Management for Heterogeneous Networks Using Deep Learning Approach
by Ishtiaq Ahmad, Sajjad Hussain, Sarmad Nozad Mahmood, Hala Mostafa, Ahmed Alkhayyat, Mohamed Marey, Ali Hashim Abbas and Zainab Abdulateef Rashed
Information 2023, 14(2), 139; https://doi.org/10.3390/info14020139 - 20 Feb 2023
Cited by 21 | Viewed by 2055
Abstract
The co-channel interference for mobile users (MUs) of a public safety network (PSN) in the co-existence of heterogeneous networks such as unmanned aerial vehicles (UAVs) and LTE-based railway networks (LRNs) needs a thorough investigation, where UAVs are deployed as mobile base stations (BSs) [...] Read more.
The co-channel interference for mobile users (MUs) of a public safety network (PSN) in the co-existence of heterogeneous networks such as unmanned aerial vehicles (UAVs) and LTE-based railway networks (LRNs) needs a thorough investigation, where UAVs are deployed as mobile base stations (BSs) for cell-edge coverage enhancement. Moreover, the LRN is employed for the train, and its control signal demands high reliability and low latency. It is necessary to provide higher priority to LRN users when allocating resources from shared radio access channels (RACs). By considering both sharing and non-sharing of RACs, co-channel interference was analyzed in the downlink network of the PSN, UAV, and LRN. By offloading more PSN MUs to the LRN or UAVs, the resource utilization of the LRN and UAV BSs was enhanced. In this paper, we aimed to adopt deep-learning (DL)-based enhanced inter-cell interference coordination (eICIC) and further enhanced ICIC (FeICIC) strategies to deal with the interference from the PSN to the LRN and UAVs. Moreover, a DL-based coordinated multipoint (CoMP) for coordinated scheduling technique was utilized along with FeICIC and eICIC to enhance the performance of PSN MUs. In the simulation results, the performance of DL-based interference management was compared with simple eICI, FeICIC, and coordinated scheduling CoMP. The DL-based FeICIC and CoMP for coordinated scheduling performed best with shared RACs. Full article
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23 pages, 6031 KiB  
Article
Information and Entropy Aspects of the Specifics of Regional Road Traffic Accident Rate in Russia
by Artur I. Petrov
Information 2023, 14(2), 138; https://doi.org/10.3390/info14020138 - 20 Feb 2023
Cited by 2 | Viewed by 2027
Abstract
The aim of this research is to study the specifics of the road accident rate formation processes in regions of the Russian Federation (2021) using information-entropic analysis. The typical research approaches (correlation-regression, factorial analyses, simulation modelling, etc.) do not always allow us to [...] Read more.
The aim of this research is to study the specifics of the road accident rate formation processes in regions of the Russian Federation (2021) using information-entropic analysis. The typical research approaches (correlation-regression, factorial analyses, simulation modelling, etc.) do not always allow us to identify its specificity. It is impossible to evaluate the quality of the researched process’s structure using these methods. However, this knowledge is required to understand the distinctions between high-quality road safety management and its opposite. In order to achieve the goal of the research methodology based on the use of the classical approaches of C. Shannon, the quantitative value of information entropy H was elaborated. The key components of this method are the modelling of the cause-and-effect chain of road accident rate formation and the consideration of the relative significances of individual blocks of the process in achieving the final result. During the research the required statistical data were collected and the structure of the road accident rate formation process in 82 regions of the Russian Federation in the format “Population P—Fleet of vehicles NVh—Road Traffic Accidents NRA—RTA Victims NV—Fatality Cases ND” was analyzed. The fact that the structure of the road accident rate formation process is extremely specific in different Russian regions was shown. Exactly this specificity forms the degree of ambiguity in the state of Russian regional road safety provision systems in terms of the probability of death in road accidents. The main conclusion of this research is that information-entropic analysis can be successfully used to assess the structural quality of road safety systems. Full article
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21 pages, 756 KiB  
Review
Reconsidering Read and Spontaneous Speech: Causal Perspectives on the Generation of Training Data for Automatic Speech Recognition
by Philipp Gabler, Bernhard C. Geiger, Barbara Schuppler and Roman Kern
Information 2023, 14(2), 137; https://doi.org/10.3390/info14020137 - 19 Feb 2023
Cited by 2 | Viewed by 2623
Abstract
Superficially, read and spontaneous speech—the two main kinds of training data for automatic speech recognition—appear as complementary, but are equal: pairs of texts and acoustic signals. Yet, spontaneous speech is typically harder for recognition. This is usually explained by different kinds of variation [...] Read more.
Superficially, read and spontaneous speech—the two main kinds of training data for automatic speech recognition—appear as complementary, but are equal: pairs of texts and acoustic signals. Yet, spontaneous speech is typically harder for recognition. This is usually explained by different kinds of variation and noise, but there is a more fundamental deviation at play: for read speech, the audio signal is produced by recitation of the given text, whereas in spontaneous speech, the text is transcribed from a given signal. In this review, we embrace this difference by presenting a first introduction of causal reasoning into automatic speech recognition, and describing causality as a tool to study speaking styles and training data. After breaking down the data generation processes of read and spontaneous speech and analysing the domain from a causal perspective, we highlight how data generation by annotation must affect the interpretation of inference and performance. Our work discusses how various results from the causality literature regarding the impact of the direction of data generation mechanisms on learning and prediction apply to speech data. Finally, we argue how a causal perspective can support the understanding of models in speech processing regarding their behaviour, capabilities, and limitations. Full article
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24 pages, 376 KiB  
Article
GATUGU: Six Perspectives of Evaluation of Gamified Systems
by Jakub Swacha, Ricardo Queirós and José Carlos Paiva
Information 2023, 14(2), 136; https://doi.org/10.3390/info14020136 - 19 Feb 2023
Cited by 4 | Viewed by 1922
Abstract
As gamification spreads to new areas, new applications are being developed and the interest in evaluating gamified systems continues to grow. To date, however, no one has comprehensively approached this topic: multiple evaluation dimensions and measures have been proposed and applied without any [...] Read more.
As gamification spreads to new areas, new applications are being developed and the interest in evaluating gamified systems continues to grow. To date, however, no one has comprehensively approached this topic: multiple evaluation dimensions and measures have been proposed and applied without any effort to organize them into a full gamut of tools for the multi-dimensional evaluation of gamified systems. This paper addresses this gap by proposing GATUGU, a set of six perspectives of evaluation of gamified systems: General effects of gamification, Area-specific effects of gamification, Technical quality of gamified systems, Use of gamified systems, Gamefulness of gamified systems, and User experience of gamified systems. For each perspective, GATUGU indicates the relevant dimensions of evaluation, and, for each dimension, one measure is suggested. GATUGU does not introduce any new measurement tools but merely recommends one of the available tools for each dimension, considering their popularity and ease of use. GATUGU can guide researchers in selecting gamification system evaluation perspectives and dimensions and in finding adequate measurement tools. Thanks to conforming to GATUGU, the published gamification system evaluation results will become easier to compare and to perform various kinds of meta-analyses on them. Full article
(This article belongs to the Special Issue Cloud Gamification 2021 & 2022)
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18 pages, 4269 KiB  
Article
Transmission of Digital Data in the 5G Era: Compression and Privacy
by Bruno Carpentieri and Francesco Palmieri
Information 2023, 14(2), 135; https://doi.org/10.3390/info14020135 - 18 Feb 2023
Cited by 1 | Viewed by 1428
Abstract
The vast majority of compressed digital data that flows nowadays on modern high-speed networks is directly related to human activity. It describes what we do, what we see and photograph, where we go, whom we meet, and specifically every moment of our lives. [...] Read more.
The vast majority of compressed digital data that flows nowadays on modern high-speed networks is directly related to human activity. It describes what we do, what we see and photograph, where we go, whom we meet, and specifically every moment of our lives. This brings up issues and concerns regarding the necessity to safeguard user privacy as well as to protect the digital multimedia contents that are delivered to offer new experiences. In this paper, we explore a unified approach to compression and privacy by considering different types of digital data (text, images, sound, and hyperspectral images). Full article
(This article belongs to the Section Information Applications)
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17 pages, 3716 KiB  
Concept Paper
Broad and Selectively Deep: An MRMPM Paradigm for Supporting Analysis
by Paul K. Davis
Information 2023, 14(2), 134; https://doi.org/10.3390/info14020134 - 18 Feb 2023
Cited by 1 | Viewed by 1138
Abstract
This paper discusses challenges for M&S if it is to be increasingly important to decision aiding and policy analysis. It suggests an approach that—from the outset of a policy analysis project—incorporates M&S of a varied resolution with the intent that (1) the results [...] Read more.
This paper discusses challenges for M&S if it is to be increasingly important to decision aiding and policy analysis. It suggests an approach that—from the outset of a policy analysis project—incorporates M&S of a varied resolution with the intent that (1) the results of analysis will be communicated with a relatively simple model and corresponding narrative that scans the system problem in breadth, having been informed by richer modeling, and (2) the broad view is supplemented by the selective detail (zooms) and selected change of the perspective as needed. This is not just a matter of “dumbing down” communication, but a matter of thinking about both forests and trees from the outset and about designing analytic tools accordingly. It will also enable exploratory analysis amidst uncertainty and disagreement, which is central to modern policy analysis and decision-aiding. All of this poses significant challenges for those who design and build M&S. Full article
(This article belongs to the Special Issue Simulation Modeling Theory and Practice: Pushing the Boundaries)
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13 pages, 2081 KiB  
Article
Named Entity Recognition Model Based on Feature Fusion
by Zhen Sun and Xinfu Li
Information 2023, 14(2), 133; https://doi.org/10.3390/info14020133 - 17 Feb 2023
Cited by 4 | Viewed by 2282
Abstract
Named entity recognition can deeply explore semantic features and enhance the ability of vector representation of text data. This paper proposes a named entity recognition method based on multi-head attention to aim at the problem of fuzzy lexical boundary in Chinese named entity [...] Read more.
Named entity recognition can deeply explore semantic features and enhance the ability of vector representation of text data. This paper proposes a named entity recognition method based on multi-head attention to aim at the problem of fuzzy lexical boundary in Chinese named entity recognition. Firstly, Word2vec is used to extract word vectors, HMM is used to extract boundary vectors, ALBERT is used to extract character vectors, the Feedforward-attention mechanism is used to fuse the three vectors, and then the fused vectors representation is used to remove features by BiLSTM. Then multi-head attention is used to mine the potential word information in the text features. Finally, the text label classification results are output after the conditional random field screening. Through the verification of WeiboNER, MSRA, and CLUENER2020 datasets, the results show that the proposed algorithm can effectively improve the performance of named entity recognition. Full article
(This article belongs to the Topic Advances in Artificial Neural Networks)
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21 pages, 2447 KiB  
Article
Conceptual Approach to the Pedagogy of Serious Games
by María Rosa Fernández-Sánchez, Alberto González-Fernández and Jesús Acevedo-Borrega
Information 2023, 14(2), 132; https://doi.org/10.3390/info14020132 - 17 Feb 2023
Cited by 1 | Viewed by 2921
Abstract
The transformation of educational processes, derived from the technological disruption that has taken place in the educational field, has allowed for the development of certain methodologies and techniques that place emphasis on the students as an active element in their own learning. Among [...] Read more.
The transformation of educational processes, derived from the technological disruption that has taken place in the educational field, has allowed for the development of certain methodologies and techniques that place emphasis on the students as an active element in their own learning. Among these methodologies is learning based on video games. Serious games are video games with an explicit educational objective, that facilitate the generation of motivating contexts, promoting relevant experiences, and with the possibility of creating challenges of a systemic nature. With a systematic literature review (SLR) methodology, this study analysed the pedagogical models and/or approaches that are implemented in the teaching–learning processes brought about by the use of serious games, with the aim of evidencing the potentialities derived from the conception of the video game as an educational resource. The results show a clear conceptual network in relation to the analysed subject, with little interaction between selected studies. A variety of pedagogical models were identified, pertaining to the use of serious games as an educational resource in the classroom context. As an overall conclusion, there is no one reference model able to generate a single pedagogy for serious games. Full article
(This article belongs to the Special Issue Artificial Intelligence and Games Science in Education)
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37 pages, 1682 KiB  
Article
Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems
by Nikzad Chizari, Keywan Tajfar and María N. Moreno-García
Information 2023, 14(2), 131; https://doi.org/10.3390/info14020131 - 17 Feb 2023
Cited by 2 | Viewed by 2443
Abstract
In today’s technology-driven society, many decisions are made based on the results provided by machine learning algorithms. It is widely known that the models generated by such algorithms may present biases that lead to unfair decisions for some segments of the population, such [...] Read more.
In today’s technology-driven society, many decisions are made based on the results provided by machine learning algorithms. It is widely known that the models generated by such algorithms may present biases that lead to unfair decisions for some segments of the population, such as minority or marginalized groups. Hence, there is concern about the detection and mitigation of these biases, which may increase the discriminatory treatments of some demographic groups. Recommender systems, used today by millions of users, are not exempt from this drawback. The influence of these systems on so many user decisions, which in turn are taken as the basis for future recommendations, contributes to exacerbating this problem. Furthermore, there is evidence that some of the most recent and successful recommendation methods, such as those based on graphical neural networks (GNNs), are more sensitive to bias. The evaluation approaches of some of these biases, as those involving protected demographic groups, may not be suitable for recommender systems since their results are the preferences of the users and these do not necessarily have to be the same for the different groups. Other assessment metrics are aimed at evaluating biases that have no impact on the user. In this work, the suitability of different user-centered bias metrics in the context of GNN-based recommender systems are analyzed, as well as the response of recommendation methods with respect to the different types of biases to which these measures are addressed. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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17 pages, 2631 KiB  
Article
CSK-CNN: Network Intrusion Detection Model Based on Two-Layer Convolution Neural Network for Handling Imbalanced Dataset
by Jiaming Song, Xiaojuan Wang, Mingshu He and Lei Jin
Information 2023, 14(2), 130; https://doi.org/10.3390/info14020130 - 16 Feb 2023
Cited by 3 | Viewed by 1723
Abstract
In computer networks, Network Intrusion Detection System (NIDS) plays a very important role in identifying intrusion behaviors. NIDS can identify abnormal behaviors by analyzing network traffic. However, the performance of classifier is not very good in identifying abnormal traffic for minority classes. In [...] Read more.
In computer networks, Network Intrusion Detection System (NIDS) plays a very important role in identifying intrusion behaviors. NIDS can identify abnormal behaviors by analyzing network traffic. However, the performance of classifier is not very good in identifying abnormal traffic for minority classes. In order to improve the detection rate on class imbalanced dataset, we propose a network intrusion detection model based on two-layer CNN and Cluster-SMOTE + K-means algorithm (CSK-CNN) to process imbalanced dataset. CSK combines the cluster based Synthetic Minority Over Sampling Technique (Cluster-SMOTE) and K-means based under sampling algorithm. Through the two-layer network, abnormal traffic can not only be identified, but also be classified into specific attack types. This paper has been verified on UNSW-NB15 dataset and CICIDS2017 dataset, and the performance of the proposed model has been evaluated using such indicators as accuracy, recall, precision, F1-score, ROC curve, AUC value, training time and testing time. The experiment shows that the proposed CSK-CNN in this paper is obviously superior to other comparison algorithms in terms of network intrusion detection performance, and is suitable for deployment in the real network environment. Full article
(This article belongs to the Special Issue Advances in Computing, Communication & Security)
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26 pages, 2762 KiB  
Article
A Blockchain-Inspired Attribute-Based Zero-Trust Access Control Model for IoT
by Samia Masood Awan, Muhammad Ajmal Azad, Junaid Arshad, Urooj Waheed and Tahir Sharif
Information 2023, 14(2), 129; https://doi.org/10.3390/info14020129 - 16 Feb 2023
Cited by 11 | Viewed by 4250
Abstract
The connected or smart environment is the integration of smart devices (sensors, IoT devices, or actuator) into the Internet of Things (IoT) paradigm, in which a large number of devices are connected, monitoring the physical environment and processes and transmitting into the centralized [...] Read more.
The connected or smart environment is the integration of smart devices (sensors, IoT devices, or actuator) into the Internet of Things (IoT) paradigm, in which a large number of devices are connected, monitoring the physical environment and processes and transmitting into the centralized database for advanced analytics and analysis. This integrated and connected setup allows greater levels of automation of smart systems than is possible with just the Internet. While delivering services to the different processes and application within connected smart systems, these IoT devices perform an impeccably large number of device-to-device communications that allow them to access the selected subsets of device information and data. The sensitive and private nature of these data renders the smart infrastructure vulnerable to copious attacks which threat agents exploit for cyberattacks which not only affect critical services but probably bring threat to people’s lives. Hence, advanced measures need to be taken for securing smart environments, such as dynamic access control, advanced network screening, and monitoring behavioural anomalies. In this paper, we have discussed the essential cyberthreats and vulnerabilities in smart environments and proposed ZAIB (Zero-Trust and ABAC for IoT using Blockchain), a novel secure framework that monitors and facilitates device-to-device communications with different levels of access-controlled mechanisms based on environmental parameters and device behaviour. It is protected by zero-trust architecture and provides dynamic behavioural analysis of IoT devices by calculating device trust levels for each request. ZAIB enforces variable policies specifically generated for each scenario by using attribute-based access control (ABAC). We have used blockchain to ensure anonymous device and user registrations and immutable activity logs. All the attributes, trust level histories, and data generated by IoT devices are protected using IPFS. Finally, a security evaluation shows that ZAIB satisfies the needs of active defence and end-to-end security enforcement of data, users, and services involved in a smart grid network. Full article
(This article belongs to the Special Issue Pervasive Computing in IoT)
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21 pages, 2158 KiB  
Article
Adaptive Savitzky–Golay Filters for Analysis of Copy Number Variation Peaks from Whole-Exome Sequencing Data
by Peter Juma Ochieng, Zoltán Maróti, József Dombi, Miklós Krész, József Békési and Tibor Kalmár
Information 2023, 14(2), 128; https://doi.org/10.3390/info14020128 - 16 Feb 2023
Cited by 2 | Viewed by 1922
Abstract
Copy number variation (CNV) is a form of structural variation in the human genome that provides medical insight into complex human diseases; while whole-genome sequencing is becoming more affordable, whole-exome sequencing (WES) remains an important tool in clinical diagnostics. Because of its discontinuous [...] Read more.
Copy number variation (CNV) is a form of structural variation in the human genome that provides medical insight into complex human diseases; while whole-genome sequencing is becoming more affordable, whole-exome sequencing (WES) remains an important tool in clinical diagnostics. Because of its discontinuous nature and unique characteristics of sparse target-enrichment-based WES data, the analysis and detection of CNV peaks remain difficult tasks. The Savitzky–Golay (SG) smoothing is well known as a fast and efficient smoothing method. However, no study has documented the use of this technique for CNV peak detection. It is well known that the effectiveness of the classical SG filter depends on the proper selection of the window length and polynomial degree, which should correspond with the scale of the peak because, in the case of peaks with a high rate of change, the effectiveness of the filter could be restricted. Based on the Savitzky–Golay algorithm, this paper introduces a novel adaptive method to smooth irregular peak distributions. The proposed method ensures high-precision noise reduction by dynamically modifying the results of the prior smoothing to automatically adjust parameters. Our method offers an additional feature extraction technique based on density and Euclidean distance. In comparison to classical Savitzky–Golay filtering and other peer filtering methods, the performance evaluation demonstrates that adaptive Savitzky–Golay filtering performs better. According to experimental results, our method effectively detects CNV peaks across all genomic segments for both short and long tags, with minimal peak height fidelity values (i.e., low estimation bias). As a result, we clearly demonstrate how well the adaptive Savitzky–Golay filtering method works and how its use in the detection of CNV peaks can complement the existing techniques used in CNV peak analysis. Full article
(This article belongs to the Special Issue Biomedical Signal Processing and Data Analytics in Healthcare Systems)
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13 pages, 630 KiB  
Review
Subtask Segmentation Methods of the Timed Up and Go Test and L Test Using Inertial Measurement Units—A Scoping Review
by Alexis L. McCreath Frangakis, Edward D. Lemaire and Natalie Baddour
Information 2023, 14(2), 127; https://doi.org/10.3390/info14020127 - 16 Feb 2023
Cited by 1 | Viewed by 1441
Abstract
The Timed Up and Go test (TUG) and L Test are functional mobility tests that allow healthcare providers to assess a person’s balance and fall risk. Segmenting these mobility tests into their respective subtasks, using sensors, can provide further and more precise information [...] Read more.
The Timed Up and Go test (TUG) and L Test are functional mobility tests that allow healthcare providers to assess a person’s balance and fall risk. Segmenting these mobility tests into their respective subtasks, using sensors, can provide further and more precise information on mobility status. To identify and compare current methods for subtask segmentation using inertial sensor data, a scoping review of the literature was conducted using PubMed, Scopus, and Google Scholar. Articles were identified that described subtask segmentation methods for the TUG and L Test using only inertial sensor data. The filtering method, ground truth estimation device, demographic, and algorithm type were compared. One article segmenting the L Test and 24 articles segmenting the TUG met the criteria. The articles were published between 2008 and 2022. Five studies used a mobile smart device’s inertial measurement system, while 20 studies used a varying number of external inertial measurement units. Healthy adults, people with Parkinson’s Disease, and the elderly were the most common demographics. A universally accepted method for segmenting the TUG test and the L Test has yet to be published. Angular velocity in the vertical and mediolateral directions were common signals for subtask differentiation. Increasing sample sizes and furthering the comparison of segmentation methods with the same test sets will allow us to expand the knowledge generated from these clinically accessible tests. Full article
(This article belongs to the Special Issue Advanced Computer and Digital Technologies)
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13 pages, 573 KiB  
Article
Security Verification of an Authentication Algorithm Based on Verifiable Encryption
by Maki Kihara and Satoshi Iriyama
Information 2023, 14(2), 126; https://doi.org/10.3390/info14020126 - 15 Feb 2023
Viewed by 1397
Abstract
A new class of cryptosystems called verifiable encryption (VE) that facilitates the verification of two plaintexts without decryption was proposed in our previous paper. The main contributions of our previous study include the following. (1) Certain cryptosystems such as the one-time pad belong [...] Read more.
A new class of cryptosystems called verifiable encryption (VE) that facilitates the verification of two plaintexts without decryption was proposed in our previous paper. The main contributions of our previous study include the following. (1) Certain cryptosystems such as the one-time pad belong to the VE class. (2) We constructed an authentication algorithm for unlocking local devices via a network that utilizes the property of VE. (3) As a result of implementing the VE-based authentication algorithm using the one-time pad, the encryption, verification, and decryption processing times are less than 1 ms even with a text length of 8192 bits. All the personal information used in the algorithm is protected by Shanon’s perfect secrecy. (4) The robustness of the algorithm against man-in-the-middle attacks and plaintext attacks was discussed. However, the discussion about the security of the algorithm was insufficient from the following two perspectives: (A) its robustness against other theoretical attacks such as ciphertext-only, known-plaintext, chosen-plaintext, adaptive chosen-plaintext, chosen-ciphertext, and adaptive chosen-ciphertext attacks was not discussed; (B) a formal security analysis using security verification tools was not performed. In this paper, we analyze the security of the VE-based authentication algorithm by discussing its robustness against the above theoretical attacks and by validating the algorithm using a security verification tool. These security analyses, show that known attacks are ineffective against the algorithm. Full article
(This article belongs to the Special Issue Internet of Things and Cloud-Fog-Edge Computing)
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15 pages, 21230 KiB  
Article
Data Augmentation Method for Pedestrian Dress Recognition in Road Monitoring and Pedestrian Multiple Information Recognition Model
by Huiyong Wang, Liang Guo, Ding Yang and Xiaoming Zhang
Information 2023, 14(2), 125; https://doi.org/10.3390/info14020125 - 15 Feb 2023
Viewed by 1226
Abstract
Road intelligence monitoring is an inevitable trend of urban intelligence, and clothing information is the main factor to identify pedestrians. Therefore, this paper establishes a multi-information clothing recognition model and proposes a data augmentation method based on road monitoring. First, we use Mask [...] Read more.
Road intelligence monitoring is an inevitable trend of urban intelligence, and clothing information is the main factor to identify pedestrians. Therefore, this paper establishes a multi-information clothing recognition model and proposes a data augmentation method based on road monitoring. First, we use Mask R-CNN to detect the clothing category information in the monitoring; then, we transfer the mask to the k-means cluster to obtain the color and finally obtain the clothing color and category. However, the monitoring scene and dataset are quite different, so a data augmentation method suitable for road monitoring is designed to improve the recognition ability of small targets and occluded targets. The small target mAP (mean average precision) recognition ability is improved by 12.37% (from 30.37%). The method of this study can help find relevant passers-by in the actual monitoring scene, which is conducive to the intelligent development of the city. Full article
(This article belongs to the Special Issue Intelligent Information Technology)
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17 pages, 492 KiB  
Article
Modeling and Moderation of COVID-19 Social Network Chat
by Félix Gélinas-Gascon and Richard Khoury
Information 2023, 14(2), 124; https://doi.org/10.3390/info14020124 - 15 Feb 2023
Cited by 1 | Viewed by 1614
Abstract
Negative social media usage during the COVID-19 pandemic has highlighted the importance of understanding the spread of misinformation and toxicity in public online discussions. In this paper, we propose a novel unsupervised method to discover the structure of online COVID-19-related conversations. Our method [...] Read more.
Negative social media usage during the COVID-19 pandemic has highlighted the importance of understanding the spread of misinformation and toxicity in public online discussions. In this paper, we propose a novel unsupervised method to discover the structure of online COVID-19-related conversations. Our method trains a nine-state Hidden Markov Model (HMM) initialized from a biclustering of 23 features extracted from online messages. We apply our method to 16,000 conversations (1.5 million messages) that took place on the Facebook pages of 15 Canadian newspapers following COVID-19 news items, and show that it can effectively extract the conversation structure and discover the main themes of the messages. Furthermore, we demonstrate how the PageRank algorithm and the conversation graph discovered can be used to simulate the impact of five different moderation strategies, which makes it possible to easily develop and test new strategies to limit the spread of harmful messages. Although our work in this paper focuses on the COVID-19 pandemic, the methodology is general enough to be applied to handle communications during future pandemics and other crises, or to develop better practices for online community moderation in general. Full article
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14 pages, 12390 KiB  
Article
Research on Pedestrian Detection Based on the Multi-Scale and Feature-Enhancement Model
by Rui Li and Yaxin Zu
Information 2023, 14(2), 123; https://doi.org/10.3390/info14020123 - 14 Feb 2023
Cited by 2 | Viewed by 1905
Abstract
Pedestrian detection represents one of the critical tasks of computer vision; however, detecting pedestrians can be compromised by problems such as the various scale of pedestrian features and cluttered background, which can easily cause a loss of accuracy. Therefore, we propose a pedestrian [...] Read more.
Pedestrian detection represents one of the critical tasks of computer vision; however, detecting pedestrians can be compromised by problems such as the various scale of pedestrian features and cluttered background, which can easily cause a loss of accuracy. Therefore, we propose a pedestrian detection method based on the FCOS network. Firstly, we designed a feature enhancement module to ensure that effective high-level semantics are obtained while preserving the detailed features of pedestrians. Secondly, we defined a key-center region judgment to reduce the interference of background information on pedestrian feature extraction. By testing on the Caltech pedestrian dataset, the AP value is improved from 87.36% to 94.16%. The results of the comparison experiment illustrate that the model proposed in this paper can significantly increase the accuracy. Full article
(This article belongs to the Special Issue Deep Learning for Human-Centric Computer Vision)
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33 pages, 3882 KiB  
Systematic Review
Intelligent Process Automation and Business Continuity: Areas for Future Research
by José Brás, Ruben Pereira and Sérgio Moro
Information 2023, 14(2), 122; https://doi.org/10.3390/info14020122 - 14 Feb 2023
Cited by 4 | Viewed by 5151
Abstract
Robotic process automation and intelligent process automation have gained a foothold in the automation of business processes, using blocks of software (bots). These agents interact with systems through interfaces, replacing human intervention with the aim of improving efficiency, reducing costs and mitigating risks [...] Read more.
Robotic process automation and intelligent process automation have gained a foothold in the automation of business processes, using blocks of software (bots). These agents interact with systems through interfaces, replacing human intervention with the aim of improving efficiency, reducing costs and mitigating risks by ensuring and enforcing compliance measures. However, there are aspects of the incorporation of this new reality within the business continuity lifecycle that are still unclear, and which need to be evaluated. This study provides a multivocal literature review of robotic process automation and intelligent process automation correlated with business continuity, to identify the level of awareness of these two emerging forms of automation within the business continuity management lifecycle. Based on the reviewed literature, the study develops a discussion of the main research areas for investigation, identifying what is attracting the attention of practitioners and researchers and which areas they highlight as promising for future research. Numerous sources from relevant backgrounds reveal an interest in these interrelated topics but there as yet is little or no information available on the direct connection between them. Full article
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18 pages, 3061 KiB  
Article
Modeling Sociodynamic Processes Based on the Use of the Differential Diffusion Equation with Fractional Derivatives
by Liliya A. Demidova, Dmitry O. Zhukov, Elena G. Andrianova and Alexander S. Sigov
Information 2023, 14(2), 121; https://doi.org/10.3390/info14020121 - 13 Feb 2023
Cited by 1 | Viewed by 941
Abstract
This paper explores the social dynamics of processes in complex systems involving humans by focusing on user activity in online media outlets. The R/S analysis showed that the time series of the processes under consideration are fractal and anti-persistent (they have a short-term [...] Read more.
This paper explores the social dynamics of processes in complex systems involving humans by focusing on user activity in online media outlets. The R/S analysis showed that the time series of the processes under consideration are fractal and anti-persistent (they have a short-term memory and a Hurst exponent significantly less than 0.5). Following statistical processing, the observed data showed that there is a small amount of asymmetry in the distribution of user activity change amplitudes in news comments; the amplitude distribution is almost symmetrical, but there is a heavy tail as the probability plots lie above the normal probability plot. The fractality of the time series for the observed processes could be due to the variables describing them (the time and level of a series), which are characterized by fractional variables of measurement. Therefore, when figuring out how to approximate functions to determine the probability density of their parameters, it is advisable to use fractional differential equations, such as those of the diffusion type. This paper describes the development of such a model and uses the observed data to analyze and compare the modeling results. Full article
(This article belongs to the Section Information Applications)
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9 pages, 883 KiB  
Article
Structure Fault Tolerance of Bubble-Sort Star Graphs
by Lantao You, Jianfeng Jiang and Yuejuan Han
Information 2023, 14(2), 120; https://doi.org/10.3390/info14020120 - 13 Feb 2023
Viewed by 1149
Abstract
As two significant performance indicators, structure connectivity and substructure connectivity have been widely studied, and they are used to judge a network’s fault tolerance properties from the perspective of the structure becoming faulty. An n-dimensional bubble-sort star graph BSn is [...] Read more.
As two significant performance indicators, structure connectivity and substructure connectivity have been widely studied, and they are used to judge a network’s fault tolerance properties from the perspective of the structure becoming faulty. An n-dimensional bubble-sort star graph BSn is a popular interconnection network with many good properties. We find the upper bounds of κ(BSn;K1,3) and κs(BSn;K1,3) in this paper. Furthermore, we establish κ(BSn;H) and κs(BSn;H) of BSn, where H{K1,K1,1,K1,2}. Full article
(This article belongs to the Section Information Processes)
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11 pages, 2922 KiB  
Article
FuseLGNet: Fusion of Local and Global Information for Detection of Parkinson’s Disease
by Ming Chen, Tao Ren, Pihai Sun, Jianfei Wu, Jinfeng Zhang and Aite Zhao
Information 2023, 14(2), 119; https://doi.org/10.3390/info14020119 - 13 Feb 2023
Viewed by 1232
Abstract
In the past few years, the assessment of Parkinson’s disease (PD) has mainly been based on the clinician’s examination, the patient’s medical history, and self-report. Parkinson’s disease may be misdiagnosed due to a lack of clinical experience. Moreover, it is highly subjective and [...] Read more.
In the past few years, the assessment of Parkinson’s disease (PD) has mainly been based on the clinician’s examination, the patient’s medical history, and self-report. Parkinson’s disease may be misdiagnosed due to a lack of clinical experience. Moreover, it is highly subjective and is not conducive to reflecting a true result. Due to the high incidence rate and increasing trend of PD, it is significant to use objective monitoring and diagnostic tools for accurate and timely diagnosis. In this paper, we designed a low-level feature extractor that uses convolutional layers to extract local information about an image and a high-level feature extractor that extracts global information about an image through the autofocus mechanism. PD is detected by fusing local and global information. The model is trained and evaluated on two publicly available datasets. Experiments have shown that our model has a strong advantage in diagnosing whether people have PD; gait-based analysis and recognition can also provide effective evidence for the early diagnosis of PD. Full article
(This article belongs to the Special Issue Advances in Medical Image Analysis and Deep Learning)
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20 pages, 868 KiB  
Article
How a Rubric Score Application Empowers Teachers’ Attitudes over Computational Thinking Leverage
by Ioannis Dimos, Chrysoula Velaora, Konstantinos Louvaris, Athanasios Kakarountas and Assimina Antonarakou
Information 2023, 14(2), 118; https://doi.org/10.3390/info14020118 - 13 Feb 2023
Cited by 3 | Viewed by 1641
Abstract
Computational Thinking (CT) has emerged as an umbrella term that refers to a broad set of problem-solving skills. New generations must conquer these skills in order to thrive in a computer-based world. Teachers, as agents of change, must also be familiar, trained and [...] Read more.
Computational Thinking (CT) has emerged as an umbrella term that refers to a broad set of problem-solving skills. New generations must conquer these skills in order to thrive in a computer-based world. Teachers, as agents of change, must also be familiar, trained and well-prepared in order to train children in CT. This paper examines STEM (Science, Technology, Engineering and Mathematics) and non-STEM teachers’ attitudes and readiness to adopt and utilize Computational Thinking concepts in the curriculum. The research was conducted through a descriptive assessment of students using thematically related criteria (rubrics) and a criterion on Computational Thinking usage and utilization. Fifteen teachers (n = 15) were invited to a focus group discussion in which they were asked to complete a questionnaire and, subsequently, to openly analyze their answers. The results show that the majority of teachers used computational thinking as an assessment criterion and stated that they did not face any significant problems with it. At the end of the focus group questions, they concluded that they consider participation in a training program regarding the concept and principles of computational thinking and the way they could integrate into the educational process necessary. Teachers expressed their confidence in using a set of criteria (rubric) to make students’ assessments more effective and stated that they can easily use at least one criterion for Computational Thinking. Full article
(This article belongs to the Special Issue Information Technologies in Education, Research and Innovation)
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19 pages, 750 KiB  
Review
Smart Contracts in Blockchain Technology: A Critical Review
by Hamed Taherdoost
Information 2023, 14(2), 117; https://doi.org/10.3390/info14020117 - 13 Feb 2023
Cited by 40 | Viewed by 26180
Abstract
By utilizing smart contracts, which are essentially scripts that are anchored in a decentralized manner on blockchains or other similar infrastructures, it is possible to make the execution of predetermined procedures visible to the outside world. The programmability of previously unrealized assets, such [...] Read more.
By utilizing smart contracts, which are essentially scripts that are anchored in a decentralized manner on blockchains or other similar infrastructures, it is possible to make the execution of predetermined procedures visible to the outside world. The programmability of previously unrealized assets, such as money, and the automation of previously manual business logic are both made possible by smart contracts. This revelation inspired us to analyze smart contracts in blockchain technologies written in English between 2012 and 2022. The scope of research is limited to the journal. Reviews, conferences, book chapters, theses, monographs, and interview-based works, and also articles in the press, are eliminated. This review comprises 252 articles over the last ten years with “Blockchain”, “block-chain”, “smart contracts”, and “smart contracts” as keywords. This paper discusses smart contracts’ present status and significance in blockchain technology. The gaps and challenges in the relevant literature have also been discussed, particularly emphasizing the limitations. Based on these findings, several research problems and prospective research routes for future study that will likely be valuable to academics and professionals are identified. Full article
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16 pages, 2874 KiB  
Article
Market Analysis with Business Intelligence System for Marketing Planning
by Treerak Kongthanasuwan, Nakarin Sriwiboon, Banpot Horbanluekit, Wasakorn Laesanklang and Tipaluck Krityakierne
Information 2023, 14(2), 116; https://doi.org/10.3390/info14020116 - 13 Feb 2023
Cited by 1 | Viewed by 4894
Abstract
The automotive and auto parts industries are important economic sectors in Thailand. With rapidly changing technology, every organization should understand what needs to be improved clearly, and shift their strategies to meet evolving consumer demands. The purpose of this research is to develop [...] Read more.
The automotive and auto parts industries are important economic sectors in Thailand. With rapidly changing technology, every organization should understand what needs to be improved clearly, and shift their strategies to meet evolving consumer demands. The purpose of this research is to develop a Business Intelligence system for a brake pad manufacturing company in Thailand. By analyzing the relationship between market demand and supply components of the company through regression analysis and the principles of the marketing mix, we develop a product lifecycle curve for forecasting product sales. The developed system increases the workflow efficiency of the case study company, being able to simplify the traditional data preparation process that requires employees to collect and summarize data every time a request is made. An intelligence dashboard is subsequently created to help support decision-making, facilitate communication within the company, and eventually improve team efficiency and productivity. Full article
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11 pages, 1259 KiB  
Article
Improved Feature Extraction and Similarity Algorithm for Video Object Detection
by Haotian You, Yufang Lu and Haihua Tang
Information 2023, 14(2), 115; https://doi.org/10.3390/info14020115 - 12 Feb 2023
Viewed by 1838
Abstract
Video object detection is an important research direction of computer vision. The task of video object detection is to detect and classify moving objects in a sequence of images. Based on the static image object detector, most of the existing video object detection [...] Read more.
Video object detection is an important research direction of computer vision. The task of video object detection is to detect and classify moving objects in a sequence of images. Based on the static image object detector, most of the existing video object detection methods use the unique temporal correlation of video to solve the problem of missed detection and false detection caused by moving object occlusion and blur. Another video object detection model guided by an optical flow network is widely used. Feature aggregation of adjacent frames is performed by estimating the optical flow field. However, there are many redundant computations for feature aggregation of adjacent frames. To begin with, this paper improved Faster RCNN by Feature Pyramid and Dynamic Region Aware Convolution. Then the S-SELSA module is proposed from the perspective of semantic and feature similarity. Feature similarity is obtained by a modified SSIM algorithm. The module can aggregate the features of frames globally to avoid redundancy. Finally, the experimental results on the ImageNet VID and DET datasets show that the mAP of the method proposed in this paper is 83.55%, which is higher than the existing methods. Full article
(This article belongs to the Special Issue Computer Vision for Security Applications)
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40 pages, 776 KiB  
Article
Making Sense of Solid for Data Governance and GDPR
by Harshvardhan J. Pandit
Information 2023, 14(2), 114; https://doi.org/10.3390/info14020114 - 12 Feb 2023
Cited by 5 | Viewed by 2728
Abstract
Solid is a new radical paradigm based on decentralising control of data from central organisations to individuals that seeks to empower individuals to have active control of who and how their data is being used. In order to realise this vision, the use-cases [...] Read more.
Solid is a new radical paradigm based on decentralising control of data from central organisations to individuals that seeks to empower individuals to have active control of who and how their data is being used. In order to realise this vision, the use-cases and implementations of Solid also require us to be consistent with the relevant privacy and data protection regulations such as the GDPR. However, to do so first requires a prior understanding of all actors, roles, and processes involved in a use-case, which then need to be aligned with GDPR’s concepts to identify relevant obligations, and then investigate their compliance. To assist with this process, we describe Solid as a variation of ‘cloud technology’ and adapt the existing standardised terminologies and paradigms from ISO/IEC standards. We then investigate the applicability of GDPR’s requirements to Solid-based implementations, along with an exploration of how existing issues arising from GDPR enforcement also apply to Solid. Finally, we outline the path forward through specific extensions to Solid’s specifications that mitigate known issues and enable the realisation of its benefits. Full article
(This article belongs to the Section Information Security and Privacy)
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12 pages, 813 KiB  
Article
Sentiment Analysis on Multimodal Transportation during the COVID-19 Using Social Media Data
by Xu Chen, Zihe Wang and Xuan Di
Information 2023, 14(2), 113; https://doi.org/10.3390/info14020113 - 10 Feb 2023
Cited by 5 | Viewed by 1925
Abstract
This paper aims to leverage Twitter data to understand travel mode choices during the pandemic. Tweets related to different travel modes in New York City (NYC) are fetched from Twitter in the two most recent years (January 2020–January 2022). Building on these data, [...] Read more.
This paper aims to leverage Twitter data to understand travel mode choices during the pandemic. Tweets related to different travel modes in New York City (NYC) are fetched from Twitter in the two most recent years (January 2020–January 2022). Building on these data, we develop travel mode classifiers, adapted from natural language processing (NLP) models, to determine whether individual tweets are related to some travel mode (subway, bus, bike, taxi/Uber, and private vehicle). Sentiment analysis is performed to understand people’s attitudinal changes about mode choices during the pandemic. Results show that a majority of people had a positive attitude toward buses, bikes, and private vehicles, which is consistent with the phenomenon of many commuters shifting away from subways to buses, bikes and private vehicles during the pandemic. We analyze negative tweets related to travel modes and find that people were worried about those who did not wear masks on subways and buses. Based on users’ demographic information, we conduct regression analysis to analyze what factors affected people’s attitude toward public transit. We find that the attitude of users in the service industry was more easily affected by MTA subway service during the pandemic. Full article
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11 pages, 2871 KiB  
Article
Blind Estimation of Spreading Code Sequence of QPSK-DSSS Signal Based on Fast-ICA
by Lu Xu, Xiaxia Liu and Yijia Zhang
Information 2023, 14(2), 112; https://doi.org/10.3390/info14020112 - 10 Feb 2023
Cited by 1 | Viewed by 1501
Abstract
Most of the existing estimation methods of spreading code sequence are not suitable for the QPSK-DSSS. We propose a spreading code sequence estimation method based on fast independent component analysis (Fast-ICA). It mainly includes signal preprocessing, calculations of separation matrix, and spreading code [...] Read more.
Most of the existing estimation methods of spreading code sequence are not suitable for the QPSK-DSSS. We propose a spreading code sequence estimation method based on fast independent component analysis (Fast-ICA). It mainly includes signal preprocessing, calculations of separation matrix, and spreading code sequence. Firstly, the received signal is segmented according to the period of the spreading code sequence, and the covariance matrix can be calculated. Then, the signal subspace and corresponding eigenvalues are obtained by eigenvalue decomposition of the covariance matrix. Subsequently, the received signal matrix needs to be whitened. Finally, the Fast-ICA algorithm is used to find the separation matrix to estimate the in-phase and orthogonal spreading code sequence. The experiment result shows that the estimation of the spreading code sequence can be carried out based on Fast-ICA under a low SNR of −12 dB. Compared with the constant modulus algorithm (CMA) and the decomposition method for the real part of the self-covariance matrix (EVD-R), this method has a better performance. Full article
(This article belongs to the Special Issue Advances in Wireless Communications Systems)
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32 pages, 423 KiB  
Review
A Comprehensive Taxonomy for Prediction Models in Software Engineering
by Xinli Yang, Jingjing Liu and Denghui Zhang
Information 2023, 14(2), 111; https://doi.org/10.3390/info14020111 - 10 Feb 2023
Cited by 1 | Viewed by 1848
Abstract
Applying prediction models to software engineering is an interesting research area. There have been many related studies which leverage prediction models to achieve good performance in various software engineering tasks. With more and more researches in software engineering leverage prediction models, there is [...] Read more.
Applying prediction models to software engineering is an interesting research area. There have been many related studies which leverage prediction models to achieve good performance in various software engineering tasks. With more and more researches in software engineering leverage prediction models, there is a need to sort out related studies, aiming to summarize which software engineering tasks prediction models can apply to and how to better leverage prediction models in these tasks. This article conducts a comprehensive taxonomy on prediction models applied to software engineering. We review 136 papers from top conference proceedings and journals in the last decade and summarize 11 research topics prediction models can apply to. Based on the papers, we conclude several big challenges and directions. We believe that the comprehensive taxonomy will help us understand the research area deeper and infer several useful and practical implications. Full article
(This article belongs to the Special Issue Data Security and Privacy Protection in Cloud Computing)
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24 pages, 560 KiB  
Article
Application of Meta-Gaming Concept to the Publishing Platform: Analysis of the Steam Games Platform
by Muhammad Nazhif Rizani, Mohd Nor Akmal Khalid and Hiroyuki Iida
Information 2023, 14(2), 110; https://doi.org/10.3390/info14020110 - 09 Feb 2023
Viewed by 3651
Abstract
The digital marketplace has rapidly grown, transitioning the market of video games from physical localized experiences to more networked, expanded, virtual spaces. With a highly competitive business market, platform governance policy allows for the emergence of rapidly growing publishing platforms for digital video [...] Read more.
The digital marketplace has rapidly grown, transitioning the market of video games from physical localized experiences to more networked, expanded, virtual spaces. With a highly competitive business market, platform governance policy allows for the emergence of rapidly growing publishing platforms for digital video games such as the Steam platform. This study demonstrated the importance of the meta-gaming of a platform based on the Steam platform; 18,658 Steam-listed games were acquired and analyzed from the Steam Store, Steam Spy, and Steam achievement databases. A detailed analysis was conducted where key research questions were formulated concerning the game information. This study found that digital badging (i.e., achievements) increases players’ engagement with the publishing platform with good auxiliary data (such as types, rating, releases, and prices). Based on such findings, an opportunity for a new business model is envisioned. Full article
(This article belongs to the Special Issue Game Informatics)
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