Future Internet

14 pages, 5780 KiB

Open AccessArticle

Face Swapping Consistency Transfer with Neural Identity Carrier

by Kunlin Liu, Ping Wang, Wenbo Zhou, Zhenyu Zhang, Yanhao Ge, Honggu Liu, Weiming Zhang and Nenghai Yu

Future Internet 2021, 13(11), 298; https://doi.org/10.3390/fi13110298 - 22 Nov 2021

Cited by 3 | Viewed by 3325

Abstract

Deepfake aims to swap a face of an image with someone else’s likeness in a reasonable manner. Existing methods usually perform deepfake frame by frame, thus ignoring video consistency and producing incoherent results. To address such a problem, we propose a novel framework [...] Read more.

Deepfake aims to swap a face of an image with someone else’s likeness in a reasonable manner. Existing methods usually perform deepfake frame by frame, thus ignoring video consistency and producing incoherent results. To address such a problem, we propose a novel framework Neural Identity Carrier (NICe), which learns identity transformation from an arbitrary face-swapping proxy via a U-

N e t

. By modeling the incoherence between frames as noise, NICe naturally suppresses its disturbance and preserves primary identity information. Concretely, NICe inputs the original frame and learns transformation supervised by swapped pseudo labels. As the temporal incoherence has an uncertain or stochastic pattern, NICe can filter out such outliers and well maintain the target content by uncertainty prediction. With the predicted temporally stable appearance, NICe enhances its details by constraining 3D geometry consistency, making NICe learn fine-grained facial structure across the poses. In this way, NICe guarantees the temporal stableness of deepfake approaches and predicts detailed results against over-smoothness. Extensive experiments on benchmarks demonstrate that NICe significantly improves the quality of existing deepfake methods on video-level. Besides, data generated by our methods can benefit video-level deepfake detection methods. Full article

(This article belongs to the Special Issue Digital and Social Media in the Disinformation Age)

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13 pages, 8476 KiB

Open AccessArticle

Detection of Induced Activity in Social Networks: Model and Methodology

by Dmitrii Gavra, Ksenia Namyatova and Lidia Vitkova

Future Internet 2021, 13(11), 297; https://doi.org/10.3390/fi13110297 - 22 Nov 2021

Cited by 4 | Viewed by 2700

Abstract

This paper examines the problem of social media special operations and especially induced support in social media during political election campaigns. The theoretical background of the paper is based on the study fake activity in social networks during pre-election processes and the existing [...] Read more.

This paper examines the problem of social media special operations and especially induced support in social media during political election campaigns. The theoretical background of the paper is based on the study fake activity in social networks during pre-election processes and the existing models and methods of detection of such activity. The article proposes a methodology for identifying and diagnosing induced support for a political project. The methodology includes a model of induced activity, an algorithm for segmenting the audience of a political project, and a technique for detecting and diagnosing induced support. The proposed methodology provides identification of network combatants, participants of social media special operations, influencing public opinion in the interests of a political project. The methodology can be used to raise awareness of the electorate, the public, and civil society in general about the presence of artificial activity on the page of a political project. Full article

(This article belongs to the Special Issue Selected Papers from the 9th Annual Conference "Comparative Media Studies in Today's World" (CMSTW'2021))

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11 pages, 801 KiB

Open AccessArticle

Community Formation as a Byproduct of a Recommendation System: A Simulation Model for Bubble Formation in Social Media

by Franco Bagnoli, Guido de Bonfioli Cavalcabo’, Banedetto Casu and Andrea Guazzini

Future Internet 2021, 13(11), 296; https://doi.org/10.3390/fi13110296 - 22 Nov 2021

Cited by 3 | Viewed by 2694

Abstract

We investigate the problem of the formation of communities of users that selectively exchange messages among them in a simulated environment. This closed community can be seen as the prototype of the bubble effect, i.e., the isolation of individuals from other communities. We [...] Read more.

We investigate the problem of the formation of communities of users that selectively exchange messages among them in a simulated environment. This closed community can be seen as the prototype of the bubble effect, i.e., the isolation of individuals from other communities. We develop a computational model of a society, where each individual is represented as a simple neural network (a perceptron), under the influence of a recommendation system that honestly forward messages (posts) to other individuals that in the past appreciated previous messages from the sender, i.e., that showed a certain degree of affinity. This dynamical affinity database determines the interaction network. We start from a set of individuals with random preferences (factors), so that at the beginning, there is no community structure at all. We show that the simple effect of the recommendation system is not sufficient to induce the isolation of communities, even when the database of user–user affinity is based on a small sample of initial messages, subject to small-sampling fluctuations. On the contrary, when the simulated individuals evolve their internal factors accordingly with the received messages, communities can emerge. This emergence is stronger the slower the evolution of individuals, while immediate convergence favors to the breakdown of the system in smaller communities. In any case, the final communities are strongly dependent on the sequence of messages, since one can get different final communities starting from the same initial distribution of users’ factors, changing only the order of users emitting messages. In other words, the main outcome of our investigation is that the bubble formation depends on users’ evolution and is strongly dependent on early interactions. Full article

(This article belongs to the Special Issue Selected Papers from the 9th Annual Conference "Comparative Media Studies in Today's World" (CMSTW'2021))

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17 pages, 594 KiB

Open AccessArticle

Detection of Hidden Communities in Twitter Discussions of Varying Volumes

by Ivan Blekanov, Svetlana S. Bodrunova and Askar Akhmetov

Future Internet 2021, 13(11), 295; https://doi.org/10.3390/fi13110295 - 20 Nov 2021

Cited by 11 | Viewed by 3088

Abstract

The community-based structure of communication on social networking sites has long been a focus of scholarly attention. However, the problem of discovery and description of hidden communities, including defining the proper level of user aggregation, remains an important problem not yet resolved. Studies [...] Read more.

The community-based structure of communication on social networking sites has long been a focus of scholarly attention. However, the problem of discovery and description of hidden communities, including defining the proper level of user aggregation, remains an important problem not yet resolved. Studies of online communities have clear social implications, as they allow for assessment of preference-based user grouping and the detection of socially hazardous groups. The aim of this study is to comparatively assess the algorithms that effectively analyze large user networks and extract hidden user communities from them. The results we have obtained show the most suitable algorithms for Twitter datasets of different volumes (dozen thousands, hundred thousands, and millions of tweets). We show that the Infomap and Leiden algorithms provide for the best results overall, and we advise testing a combination of these algorithms for detecting discursive communities based on user traits or views. We also show that the generalized K-means algorithm does not apply to big datasets, while a range of other algorithms tend to prioritize the detection of just one big community instead of many that would mirror the reality better. For isolating overlapping communities, the GANXiS algorithm should be used, while OSLOM is not advised. Full article

(This article belongs to the Special Issue Selected Papers from the 9th Annual Conference "Comparative Media Studies in Today's World" (CMSTW'2021))

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22 pages, 593 KiB

Open AccessArticle

PECSA: Practical Edge Computing Service Architecture Applicable to Adaptive IoT-Based Applications

by Jianhua Liu and Zibo Wu

Future Internet 2021, 13(11), 294; https://doi.org/10.3390/fi13110294 - 19 Nov 2021

Cited by 1 | Viewed by 2643

Abstract

The cloud-based Internet of Things (IoT-Cloud) combines the advantages of the IoT and cloud computing, which not only expands the scope of cloud computing but also enhances the data processing capability of the IoT. Users always seek affordable and efficient services, which can [...] Read more.

The cloud-based Internet of Things (IoT-Cloud) combines the advantages of the IoT and cloud computing, which not only expands the scope of cloud computing but also enhances the data processing capability of the IoT. Users always seek affordable and efficient services, which can be completed by the cooperation of all available network resources, such as edge computing nodes. However, current solutions exhibit significant security and efficiency problems that must be solved. Insider attacks could degrade the performance of the IoT-Cloud due to its natural environment and inherent open construction. Unfortunately, traditional security approaches cannot defend against these attacks effectively. In this paper, a novel practical edge computing service architecture (PECSA), which integrates a trust management methodology with dynamic cost evaluation schemes, is proposed to address these problems. In the architecture, the edge network devices and edge platform cooperate to achieve a shorter response time and/or less economic costs, as well as to enhance the effectiveness of the trust management methodology, respectively. To achieve faster responses for IoT-based requirements, all the edge computing devices and cloud resources cooperate in a reasonable way by evaluating computational cost and runtime resource capacity in the edge networks. Moreover, when cooperated with the edge platform, the edge networks compute trust values of linked nodes and find the best collaborative approach for each user to meet various service requirements. Experimental results demonstrate the efficiency and the security of the proposed architecture. Full article

(This article belongs to the Section Network Virtualization and Edge/Fog Computing)

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18 pages, 3104 KiB

Open AccessReview

Resilience in the Cyberworld: Definitions, Features and Models

by Elisabeth Vogel, Zoya Dyka, Dan Klann and Peter Langendörfer

Future Internet 2021, 13(11), 293; https://doi.org/10.3390/fi13110293 - 19 Nov 2021

Cited by 5 | Viewed by 3353

Abstract

Resilience is a feature that is gaining more and more attention in computer science and computer engineering. However, the definition of resilience for the cyber landscape, especially embedded systems, is not yet clear. This paper discusses definitions provided by different authors, on different [...] Read more.

Resilience is a feature that is gaining more and more attention in computer science and computer engineering. However, the definition of resilience for the cyber landscape, especially embedded systems, is not yet clear. This paper discusses definitions provided by different authors, on different years and with different application areas the field of computer science/computer engineering. We identify the core statements that are more or less common to the majority of the definitions, and based on this we give a holistic definition using attributes for (cyber-) resilience. In order to pave a way towards resilience engineering, we discuss a theoretical model of the life cycle of a (cyber-) resilient system that consists of key actions presented in the literature. We adapt this model for embedded (cyber-) resilient systems. Full article

(This article belongs to the Special Issue Data Science for Cyber Security)

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17 pages, 10143 KiB

Open AccessArticle

Opportunities to Develop Lifelong Learning Tendencies in Practice-Based Teacher Education: Getting Ready for Education 4.0

by Kiomi Matsumoto-Royo, Maria Soledad Ramírez-Montoya and Paulette Conget

Future Internet 2021, 13(11), 292; https://doi.org/10.3390/fi13110292 - 19 Nov 2021

Cited by 18 | Viewed by 4740

Abstract

Education 4.0 prepares new generations to develop the skills required to perform in a technological, dynamic, and unpredictable world. The main barrier to implementing Education 4.0 in schools is that teachers have not been trained for it. Given the advances and new resources [...] Read more.

Education 4.0 prepares new generations to develop the skills required to perform in a technological, dynamic, and unpredictable world. The main barrier to implementing Education 4.0 in schools is that teachers have not been trained for it. Given the advances and new resources of the technological field, teacher preparation will be insufficient if it focuses on technological skills but does not incorporate the necessary dispositions for lifelong learning. Universities have the ethical imperative to update teacher education so teachers can become lifelong learners. The objective of this study was to understand whether practice-based curricula offer opportunities to promote lifelong learning tendencies. We used a sequential explanatory method. Quantitative and qualitative instruments were applied to pre-service teachers (survey: n = 231, semi-structured interviews: n = 8), and causal and descriptive approaches were supported by a structural equation model and constant comparative method, respectively. Data triangulation confirmed and added depth to the relationship found. Practice opportunities provided by teacher educators in learning activities and assessment tasks promote curiosity, motivation, perseverance, and self-learning regulation, when they are (i) systematic; (ii) relevant to the classroom work; (iii) presented with clear instructions and effective rubrics; (iv) accompanied with feedback focused on the task, soliciting reflection, and performed by peers and teacher educators in a trustworthy environment. This research may be of value to universities looking to renew their Education 4.0 programs because it shows that practice-based curricula not only transform pre-service teachers into teaching experts but also into lifelong learners. Full article

(This article belongs to the Special Issue Education 4.0 in the Transformation of Universities: Educational and Research Applications)

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17 pages, 1110 KiB

Open AccessArticle

Enable Fair Proof-of-Work (PoW) Consensus for Blockchains in IoT by Miner Twins (MinT)

by Qian Qu, Ronghua Xu, Yu Chen, Erik Blasch and Alexander Aved

Future Internet 2021, 13(11), 291; https://doi.org/10.3390/fi13110291 - 19 Nov 2021

Cited by 24 | Viewed by 2970

Abstract

Blockchain technology has been recognized as a promising solution to enhance the security and privacy of Internet of Things (IoT) and Edge Computing scenarios. Taking advantage of the Proof-of-Work (PoW) consensus protocol, which solves a computation intensive hashing puzzle, Blockchain ensures the security [...] Read more.

Blockchain technology has been recognized as a promising solution to enhance the security and privacy of Internet of Things (IoT) and Edge Computing scenarios. Taking advantage of the Proof-of-Work (PoW) consensus protocol, which solves a computation intensive hashing puzzle, Blockchain ensures the security of the system by establishing a digital ledger. However, the computation intensive PoW favors members possessing more computing power. In the IoT paradigm, fairness in the highly heterogeneous network edge environments must consider devices with various constraints on computation power. Inspired by the advanced features of Digital Twins (DT), an emerging concept that mirrors the lifespan and operational characteristics of physical objects, we propose a novel Miner Twins (MinT) architecture to enable a fair PoW consensus mechanism for blockchains in IoT environments. MinT adopts an edge-fog-cloud hierarchy. All physical miners of the blockchain are deployed as microservices on distributed edge devices, while fog/cloud servers maintain digital twins that periodically update miners’ running status. By timely monitoring of a miner’s footprint that is mirrored by twins, a lightweight Singular Spectrum Analysis (SSA)-based detection achieves the identification of individual misbehaved miners that violate fair mining. Moreover, we also design a novel Proof-of-Behavior (PoB) consensus algorithm to detect dishonest miners that collude to control a fair mining network. A preliminary study is conducted on a proof-of-concept prototype implementation, and experimental evaluation shows the feasibility and effectiveness of the proposed MinT scheme under a distributed byzantine network environment. Full article

(This article belongs to the Special Issue Security and Privacy in Blockchains and the IoT)

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17 pages, 1412 KiB

Open AccessArticle

MFCNet: Mining Features Context Network for RGB–IR Person Re-Identification

by Jing Mei, Huahu Xu, Yang Li, Minjie Bian and Yuzhe Huang

Future Internet 2021, 13(11), 290; https://doi.org/10.3390/fi13110290 - 18 Nov 2021

Cited by 1 | Viewed by 3709

Abstract

RGB–IR cross modality person re-identification (RGB–IR Re-ID) is an important task for video surveillance in poorly illuminated or dark environments. In addition to the common challenge of Re-ID, the large cross-modality variations between RGB and IR images must be considered. The existing RGB–IR [...] Read more.

RGB–IR cross modality person re-identification (RGB–IR Re-ID) is an important task for video surveillance in poorly illuminated or dark environments. In addition to the common challenge of Re-ID, the large cross-modality variations between RGB and IR images must be considered. The existing RGB–IR Re-ID methods use different network structures to learn the global shared features associated with multi-modalities. However, most global shared feature learning methods are sensitive to background clutter, and contextual feature relationships are not considered among the mined features. To solve these problems, this paper proposes a dual-path attention network architecture MFCNet. SGA (Spatial-Global Attention) module embedded in MFCNet includes spatial attention and global attention branches to mine discriminative features. First, the SGA module proposed in this paper focuses on the key parts of the input image to obtain robust features. Next, the module mines the contextual relationships among features to obtain discriminative features and improve network performance. Finally, extensive experiments demonstrate that the performance of the network architecture proposed in this paper is better than that of state-of-the-art methods under various settings. In the all-search mode of the SYSU and RegDB data sets, the rank-1 accuracy reaches 51.64% and 69.76%, respectively. Full article

(This article belongs to the Collection Machine Learning Approaches for User Identity)

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12 pages, 1893 KiB

Open AccessArticle

Person Re-Identification by Low-Dimensional Features and Metric Learning

by Xingyuan Chen, Huahu Xu, Yang Li and Minjie Bian

Future Internet 2021, 13(11), 289; https://doi.org/10.3390/fi13110289 - 18 Nov 2021

Cited by 3 | Viewed by 2741

Abstract

Person re-identification (Re-ID) has attracted attention due to its wide range of applications. Most recent studies have focused on the extraction of deep features, while ignoring color features that can remain stable, even for illumination variations and the variation in person pose. There [...] Read more.

Person re-identification (Re-ID) has attracted attention due to its wide range of applications. Most recent studies have focused on the extraction of deep features, while ignoring color features that can remain stable, even for illumination variations and the variation in person pose. There are also few studies that combine the powerful learning capabilities of deep learning with color features. Therefore, we hope to use the advantages of both to design a model with low computational resource consumption and excellent performance to solve the task of person re-identification. In this paper, we designed a color feature containing relative spatial information, namely the color feature with spatial information. Then, bidirectional long short-term memory (BLSTM) networks with an attention mechanism are used to obtain the contextual relationship contained in the hand-crafted color features. Finally, experiments demonstrate that the proposed model can improve the recognition performance compared with traditional methods. At the same time, hand-crafted features based on human prior knowledge not only reduce computational consumption compared with deep learning methods but also make the model more interpretable. Full article

(This article belongs to the Topic Big Data and Artificial Intelligence)

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14 pages, 2151 KiB

Open AccessArticle

Deepfake-Image Anti-Forensics with Adversarial Examples Attacks

by Li Fan, Wei Li and Xiaohui Cui

Future Internet 2021, 13(11), 288; https://doi.org/10.3390/fi13110288 - 17 Nov 2021

Cited by 7 | Viewed by 3582

Abstract

Many deepfake-image forensic detectors have been proposed and improved due to the development of synthetic techniques. However, recent studies show that most of these detectors are not immune to adversarial example attacks. Therefore, understanding the impact of adversarial examples on their performance is [...] Read more.

Many deepfake-image forensic detectors have been proposed and improved due to the development of synthetic techniques. However, recent studies show that most of these detectors are not immune to adversarial example attacks. Therefore, understanding the impact of adversarial examples on their performance is an important step towards improving deepfake-image detectors. This study developed an anti-forensics case study of two popular general deepfake detectors based on their accuracy and generalization. Herein, we propose the Poisson noise DeepFool (PNDF), an improved iterative adversarial examples generation method. This method can simply and effectively attack forensics detectors by adding perturbations to images in different directions. Our attacks can reduce its AUC from 0.9999 to 0.0331, and the detection accuracy of deepfake images from 0.9997 to 0.0731. Compared with state-of-the-art studies, our work provides an important defense direction for future research on deepfake-image detectors, by focusing on the generalization performance of detectors and their resistance to adversarial example attacks. Full article

(This article belongs to the Special Issue Machine Learning Integration with Cyber Security)

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45 pages, 4148 KiB

Open AccessReview

An Analysis on Contemporary MAC Layer Protocols in Vehicular Networks: State-of-the-Art and Future Directions

by Lopamudra Hota, Biraja Prasad Nayak, Arun Kumar, G. G. Md. Nawaz Ali and Peter Han Joo Chong

Future Internet 2021, 13(11), 287; https://doi.org/10.3390/fi13110287 - 17 Nov 2021

Cited by 15 | Viewed by 4193

Abstract

Traffic density around the globe is increasing on a day-to-day basis, resulting in more accidents, congestion, and pollution. The dynamic vehicular environment induces challenges in designing an efficient and reliable protocol for communication. Timely delivery of safety and non-safety messages is necessary for [...] Read more.

Traffic density around the globe is increasing on a day-to-day basis, resulting in more accidents, congestion, and pollution. The dynamic vehicular environment induces challenges in designing an efficient and reliable protocol for communication. Timely delivery of safety and non-safety messages is necessary for traffic congestion control and for avoiding road mishaps. For efficient resource sharing and optimized channel utilization, the media access control (MAC) protocol plays a vital role. An efficient MAC protocol design can provide fair channel access and can delay constraint safety message dissemination, improving road safety. This paper reviews the applications, characteristics, and challenges faced in the design of MAC protocols. A classification of the MAC protocol is presented based on contention mechanisms and channel access. The classification based on contention is oriented as contention-based, contention-free, and hybrid, whereas the classification based on channel access is categorized as distributed, centralized, cluster-based, cooperative, token-based, and random access. These are further sub-classified as single-channel and multi-channel, based on the type of channel resources they utilize. This paper gives an analysis of the objectives, mechanisms, advantages/disadvantages, and simulators used in specified protocols. Finally, the paper concludes with a discussion on the future scope and open challenges for improving the MAC protocol design. Full article

(This article belongs to the Special Issue 5G Enabling Technologies and Wireless Networking)

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20 pages, 1482 KiB

Open AccessArticle

What Went Wrong? Predictors of Contact Tracing Adoption in Italy during COVID-19 Pandemic

by Andrea Guazzini, Maria Fiorenza, Gabriele Panerai and Mirko Duradoni

Future Internet 2021, 13(11), 286; https://doi.org/10.3390/fi13110286 - 15 Nov 2021

Cited by 16 | Viewed by 6361

Abstract

Together with vaccines, contact tracing systems (CTS) have proved to be one of the best strategies to deal with the current COVID-19 pandemic. However, the adoption of such systems has been quite limited in EU countries, and Italy was no exception. The present [...] Read more.

Together with vaccines, contact tracing systems (CTS) have proved to be one of the best strategies to deal with the current COVID-19 pandemic. However, the adoption of such systems has been quite limited in EU countries, and Italy was no exception. The present research aimed to investigate the factors drawn from the most relevant psychological models in the literature, most associated with the adoption of CTS. The data analysis of the 501 surveyed answers (329 from CTS adopters) showed that knowing important others who have downloaded the CTS, CTS attitudes, CTS perceived efficacy, COVID-19 risk perception, and trust in the government and its actions influenced the adoption of the Italian CTS (52% of explained variance). These factors defined a new specific model that can be used to more effectively promote CTS adoption and thus increase the protective potential of these technologies, whose effectiveness is inevitably linked to adoption. Full article

(This article belongs to the Section Big Data and Augmented Intelligence)

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20 pages, 2259 KiB

Open AccessArticle

Distributed Hybrid Double-Spending Attack Prevention Mechanism for Proof-of-Work and Proof-of-Stake Blockchain Consensuses

by Nur Arifin Akbar, Amgad Muneer, Narmine ElHakim and Suliman Mohamed Fati

Future Internet 2021, 13(11), 285; https://doi.org/10.3390/fi13110285 - 12 Nov 2021

Cited by 47 | Viewed by 7727

Abstract

Blockchain technology is a sustainable technology that offers a high level of security for many industrial applications. Blockchain has numerous benefits, such as decentralisation, immutability and tamper-proofing. Blockchain is composed of two processes, namely, mining (the process of adding a new block or [...] Read more.

Blockchain technology is a sustainable technology that offers a high level of security for many industrial applications. Blockchain has numerous benefits, such as decentralisation, immutability and tamper-proofing. Blockchain is composed of two processes, namely, mining (the process of adding a new block or transaction to the global public ledger created by the previous block) and validation (the process of validating the new block added). Several consensus protocols have been introduced to validate blockchain transactions, Proof-of-Work (PoW) and Proof-of-Stake (PoS), which are crucial to cryptocurrencies, such as Bitcoin. However, these consensus protocols are vulnerable to double-spending attacks. Amongst these attacks, the 51% attack is the most prominent because it involves forking a blockchain to conduct double spending. Many attempts have been made to solve this issue, and examples include delayed proof-of-work (PoW) and several Byzantine fault tolerance mechanisms. These attempts, however, suffer from delay issues and unsorted block sequences. This study proposes a hybrid algorithm that combines PoS and PoW mechanisms to provide a fair mining reward to the miner/validator by conducting forking to combine PoW and PoS consensuses. As demonstrated by the experimental results, the proposed algorithm can reduce the possibility of intruders performing double mining because it requires achieving 100% dominance in the network, which is impossible. Full article

(This article belongs to the Special Issue Blockchain: Applications, Challenges, and Solutions)

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17 pages, 4340 KiB

Open AccessArticle

Intelligent Planning and Research on Urban Traffic Congestion

by Qigang Zhu, Yifan Liu, Ming Liu, Shuaishuai Zhang, Guangyang Chen and Hao Meng

Future Internet 2021, 13(11), 284; https://doi.org/10.3390/fi13110284 - 8 Nov 2021

Cited by 12 | Viewed by 6644

Abstract

For large and medium-sized cities, the planning and development of urban road networks may not keep pace with the growth of urban vehicles, resulting in traffic congestion on urban roads during peak hours. Take Jinan, a mid-sized city in China’s Shandong Province, for [...] Read more.

For large and medium-sized cities, the planning and development of urban road networks may not keep pace with the growth of urban vehicles, resulting in traffic congestion on urban roads during peak hours. Take Jinan, a mid-sized city in China’s Shandong Province, for example. In view of the daily traffic jam of the city’s road traffic, through investigation and analysis, the existing problems of the road traffic are found out. Based on real-time, daily road traffic data, combined with the existing road network and the planned road network, the application of a road intelligent transportation system is proposed. Combined with the application of a road intelligent transportation system, this paper discusses the future development of urban road traffic and puts forward improvement suggestions for road traffic planning. This paper has reference value for city development, road network construction, the application of intelligent transportation systems, and road traffic planning. Full article

(This article belongs to the Special Issue Sustainable Smart City)

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15 pages, 247 KiB

Open AccessReview

Misconfiguration in Firewalls and Network Access Controls: Literature Review

by Michael Alicea and Izzat Alsmadi

Future Internet 2021, 13(11), 283; https://doi.org/10.3390/fi13110283 - 8 Nov 2021

Cited by 6 | Viewed by 6375

Abstract

Firewalls and network access controls play important roles in security control and protection. Those firewalls may create an incorrect sense or state of protection if they are improperly configured. One of the major configuration problems in firewalls is related to misconfiguration in the [...] Read more.

Firewalls and network access controls play important roles in security control and protection. Those firewalls may create an incorrect sense or state of protection if they are improperly configured. One of the major configuration problems in firewalls is related to misconfiguration in the access control roles added to the firewall that will control network traffic. In this paper, we evaluated recent research trends and open challenges related to firewalls and access controls in general and misconfiguration problems in particular. With the recent advances in next-generation (NG) firewalls, firewall roles can be auto-generated based on networks and threats. Nonetheless, and due to the large number of roles in any medium to large networks, roles’ misconfiguration may occur for several reasons and will impact the performance of the firewall and overall network and protection efficiency. Full article

(This article belongs to the Special Issue Software Defined Networking and Cyber Security)

19 pages, 1109 KiB

Open AccessArticle

Improving Institutional Repositories through User-Centered Design: Indicators from a Focus Group

by Laura Icela González-Pérez, María Soledad Ramírez-Montoya and Francisco José García-Peñalvo

Future Internet 2021, 13(11), 282; https://doi.org/10.3390/fi13110282 - 2 Nov 2021

Cited by 5 | Viewed by 4570

Abstract

User experience with intuitive and flexible digital platforms can be enjoyable and satisfying. A strategy to deliver such an experience is to place the users at the center of the design process and analyze their beliefs and perceptions to add appropriate platform features. [...] Read more.

User experience with intuitive and flexible digital platforms can be enjoyable and satisfying. A strategy to deliver such an experience is to place the users at the center of the design process and analyze their beliefs and perceptions to add appropriate platform features. This study conducted with focus groups as a qualitative method of data collection to investigate users’ preferences and develop a new landing page for institutional repositories with attractive functionalities based on their information-structural rules. The research question was: What are the motivations and experiences of users in an academic community when publishing scientific information in an institutional repository? The focus group technique used in this study had three sessions. Results showed that 50% of the participants did not know the functionalities of the institutional repository nor its benefits. Users’ perceptions of platforms such as ResearchGate or Google Scholar that provide academic production were also identified. The findings showed that motivating an academic community to use an institutional repository requires technological functions, user guidelines that identify what can or cannot be published in open access, and training programs for open access publication practices and institutional repository use. These measures align with global strategies to strengthen the digital identities of scientific communities and thus benefit open science. Full article

(This article belongs to the Special Issue Education 4.0 in the Transformation of Universities: Educational and Research Applications)

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39 pages, 6772 KiB

Open AccessArticle

University Community Members’ Perceptions of Labels for Online Media

by Ryan Suttle, Scott Hogan, Rachel Aumaugher, Matthew Spradling, Zak Merrigan and Jeremy Straub

Future Internet 2021, 13(11), 281; https://doi.org/10.3390/fi13110281 - 31 Oct 2021

Cited by 8 | Viewed by 2233

Abstract

Fake news is prevalent in society. A variety of methods have been used in an attempt to mitigate the spread of misinformation and fake news ranging from using machine learning to detect fake news to paying fact checkers to manually fact check media [...] Read more.

Fake news is prevalent in society. A variety of methods have been used in an attempt to mitigate the spread of misinformation and fake news ranging from using machine learning to detect fake news to paying fact checkers to manually fact check media to ensure its accuracy. In this paper, three studies were conducted at two universities with different regional demographic characteristics to gain a better understanding of respondents’ perception of online media labeling techniques. The first study deals with what fields should appear on a media label. The second study looks into what types of informative labels respondents would use. The third focuses on blocking type labels. Participants’ perceptions, preferences, and results are analyzed by their demographic characteristics. Full article

(This article belongs to the Special Issue Digital and Social Media in the Disinformation Age)

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20 pages, 1339 KiB

Open AccessArticle

Configurable Hardware Core for IoT Object Detection

by Pedro R. Miranda, Daniel Pestana, João D. Lopes, Rui Policarpo Duarte, Mário P. Véstias, Horácio C. Neto and José T. de Sousa

Future Internet 2021, 13(11), 280; https://doi.org/10.3390/fi13110280 - 30 Oct 2021

Cited by 6 | Viewed by 2886

Abstract

Object detection is an important task for many applications, like transportation, security, and medical applications. Many of these applications are needed on edge devices to make local decisions. Therefore, it is necessary to provide low-cost, fast solutions for object detection. This work proposes [...] Read more.

Object detection is an important task for many applications, like transportation, security, and medical applications. Many of these applications are needed on edge devices to make local decisions. Therefore, it is necessary to provide low-cost, fast solutions for object detection. This work proposes a configurable hardware core on a field-programmable gate array (FPGA) for object detection. The configurability of the core allows its deployment on target devices with diverse hardware resources. The object detection accelerator is based on YOLO, for its good accuracy at moderate computational complexity. The solution was applied to the design of a core to accelerate the Tiny-YOLOv3, based on a CNN developed for constrained environments. However, it can be applied to other YOLO versions. The core was integrated into a full system-on-chip solution and tested with the COCO dataset. It achieved a performance from 7 to 14 FPS in a low-cost ZYNQ7020 FPGA, depending on the quantization, with an accuracy reduction from 2.1 to 1.4 points of

m A P_{50}

. Full article

(This article belongs to the Special Issue Deep Neural Networks on Reconfigurable Embedded Systems)

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18 pages, 4268 KiB

Open AccessArticle

Towards Virtuous Cloud Data Storage Using Access Policy Hiding in Ciphertext Policy Attribute-Based Encryption

by Siti Dhalila Mohd Satar, Masnida Hussin, Zurina Mohd Hanapi and Mohamad Afendee Mohamed

Future Internet 2021, 13(11), 279; https://doi.org/10.3390/fi13110279 - 30 Oct 2021

Cited by 7 | Viewed by 2525

Abstract

Managing and controlling access to the tremendous data in Cloud storage is very challenging. Due to various entities engaged in the Cloud environment, there is a high possibility of data tampering. Cloud encryption is being employed to control data access while securing Cloud [...] Read more.

Managing and controlling access to the tremendous data in Cloud storage is very challenging. Due to various entities engaged in the Cloud environment, there is a high possibility of data tampering. Cloud encryption is being employed to control data access while securing Cloud data. The encrypted data are sent to Cloud storage with an access policy defined by the data owner. Only authorized users can decrypt the encrypted data. However, the access policy of the encrypted data is in readable form, which results in privacy leakage. To address this issue, we proposed a reinforcement hiding in access policy over Cloud storage by enhancing the Ciphertext Policy Attribute-based Encryption (CP-ABE) algorithm. Besides the encryption process, the reinforced CP-ABE used logical connective operations to hide the attribute value of data in the access policy. These attributes were converted into scrambled data along with a ciphertext form that provides a better unreadability feature. It means that a two-level concealed tactic is employed to secure data from any unauthorized access during a data transaction. Experimental results revealed that our reinforced CP-ABE had a low computational overhead and consumed low storage costs. Furthermore, a case study on security analysis shows that our approach is secure against a passive attack such as traffic analysis. Full article

(This article belongs to the Special Issue Privacy in Smart Health)

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28 pages, 824 KiB

Open AccessArticle

Online Service Function Chain Deployment for Live-Streaming in Virtualized Content Delivery Networks: A Deep Reinforcement Learning Approach

by Jesús Fernando Cevallos Moreno, Rebecca Sattler, Raúl P. Caulier Cisterna, Lorenzo Ricciardi Celsi, Aminael Sánchez Rodríguez and Massimo Mecella

Future Internet 2021, 13(11), 278; https://doi.org/10.3390/fi13110278 - 29 Oct 2021

Cited by 13 | Viewed by 3671

Abstract

Video delivery is exploiting 5G networks to enable higher server consolidation and deployment flexibility. Performance optimization is also a key target in such network systems. We present a multi-objective optimization framework for service function chain deployment in the particular context of Live-Streaming in [...] Read more.

Video delivery is exploiting 5G networks to enable higher server consolidation and deployment flexibility. Performance optimization is also a key target in such network systems. We present a multi-objective optimization framework for service function chain deployment in the particular context of Live-Streaming in virtualized content delivery networks using deep reinforcement learning. We use an Enhanced Exploration, Dense-reward mechanism over a Dueling Double Deep Q Network (E2-D4QN). Our model assumes to use network function virtualization at the container level. We carefully model processing times as a function of current resource utilization in data ingestion and streaming processes. We assess the performance of our algorithm under bounded network resource conditions to build a safe exploration strategy that enables the market entry of new bounded-budget vCDN players. Trace-driven simulations with real-world data reveal that our approach is the only one to adapt to the complexity of the particular context of Live-Video delivery concerning the state-of-art algorithms designed for general-case service function chain deployment. In particular, our simulation test revealed a substantial QoS/QoE performance improvement in terms of session acceptance ratio against the compared algorithms while keeping operational costs within proper bounds. Full article

(This article belongs to the Special Issue 5G Enabling Technologies and Wireless Networking)

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15 pages, 41589 KiB

Open AccessArticle

Representing and Validating Cultural Heritage Knowledge Graphs in CIDOC-CRM Ontology

by Ghazal Faraj and András Micsik

Future Internet 2021, 13(11), 277; https://doi.org/10.3390/fi13110277 - 29 Oct 2021

Cited by 7 | Viewed by 3310

Abstract

In order to unify access to multiple heterogeneous sources of cultural heritage data, many datasets were mapped to the CIDOC-CRM ontology. CIDOC-CRM provides a formal structure and definitions for most cultural heritage concepts and their relationships. The COURAGE project includes historic data concerning [...] Read more.

In order to unify access to multiple heterogeneous sources of cultural heritage data, many datasets were mapped to the CIDOC-CRM ontology. CIDOC-CRM provides a formal structure and definitions for most cultural heritage concepts and their relationships. The COURAGE project includes historic data concerning people, organizations, cultural heritage collections, and collection items covering the period between 1950 and 1990. Therefore, CIDOC-CRM seemed the optimal choice for describing COURAGE entities, improving knowledge sharing, and facilitating the COURAGE dataset unification with other datasets. This paper introduces the results of translating the COURAGE dataset to CIDOC-CRM semantically. This mapping was implemented automatically according to predefined mapping rules. Several SPARQL queries were applied to validate the migration process manually. In addition, multiple SHACL shapes were conducted to validate the data and mapping models. Full article

(This article belongs to the Special Issue Applications of Semantic Web, Linked Open Data and Knowledge Graphs)

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11 pages, 460 KiB

Open AccessArticle

Security Challenges for Light Emitting Systems

by Louiza Hamada, Pascal Lorenz and Marc Gilg

Future Internet 2021, 13(11), 276; https://doi.org/10.3390/fi13110276 - 28 Oct 2021

Cited by 2 | Viewed by 1971

Abstract

Although visible light communication (VLC) channels are more secure than radio frequency channels, the broadcast nature of VLC links renders them open to eavesdropping. As a result, VLC networks must provide security in order to safeguard the user’s data from eavesdroppers. In the [...] Read more.

Although visible light communication (VLC) channels are more secure than radio frequency channels, the broadcast nature of VLC links renders them open to eavesdropping. As a result, VLC networks must provide security in order to safeguard the user’s data from eavesdroppers. In the literature, keyless security techniques have been developed to offer security for VLC. Even though these techniques provide strong security against eavesdroppers, they are difficult to deploy. Key generation algorithms are critical for securing wireless connections. Nonetheless, in many situations, the typical key generation methods may be quite complicated and costly. They consume scarce resources, such as bandwidth. In this paper, we propose a novel key extraction procedure that uses error-correcting coding and one time pad (OTP) to improve the security of VLC networks and the validity of data. This system will not have any interference problems with other devices. We also explain error correction while sending a message across a network, and suggest a change to the Berlekamp–Massey (BM) algorithm for error identification and assessment. Because each OOK signal frame is encrypted by a different key, the proposed protocol provides high physical layer security; it allows for key extraction based on the messages sent, so an intruder can never break the encryption system, even if the latter knows the protocol with which we encrypted the message; our protocol also enables for error transmission rate correction and bit mismatch rates with on-the-fly key fetch. The results presented in this paper were performed using MATLAB. Full article

(This article belongs to the Special Issue Mobile and Wireless Network Security and Privacy)

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20 pages, 2172 KiB

Open AccessArticle

Analytics on Anonymity for Privacy Retention in Smart Health Data

by Sevgi Arca and Rattikorn Hewett

Future Internet 2021, 13(11), 274; https://doi.org/10.3390/fi13110274 - 28 Oct 2021

Cited by 5 | Viewed by 2690

Abstract

Advancements in smart technology, wearable and mobile devices, and Internet of Things, have made smart health an integral part of modern living to better individual healthcare and well-being. By enhancing self-monitoring, data collection and sharing among users and service providers, smart health can [...] Read more.

Advancements in smart technology, wearable and mobile devices, and Internet of Things, have made smart health an integral part of modern living to better individual healthcare and well-being. By enhancing self-monitoring, data collection and sharing among users and service providers, smart health can increase healthy lifestyles, timely treatments, and save lives. However, as health data become larger and more accessible to multiple parties, they become vulnerable to privacy attacks. One way to safeguard privacy is to increase users’ anonymity as anonymity increases indistinguishability making it harder for re-identification. Still the challenge is not only to preserve data privacy but also to ensure that the shared data are sufficiently informative to be useful. Our research studies health data analytics focusing on anonymity for privacy protection. This paper presents a multi-faceted analytical approach to (1) identifying attributes susceptible to information leakages by using entropy-based measure to analyze information loss, (2) anonymizing the data by generalization using attribute hierarchies, and (3) balancing between anonymity and informativeness by our anonymization technique that produces anonymized data satisfying a given anonymity requirement while optimizing data retention. Our anonymization technique is an automated Artificial Intelligent search based on two simple heuristics. The paper describes and illustrates the detailed approach and analytics including pre and post anonymization analytics. Experiments on published data are performed on the anonymization technique. Results, compared with other similar techniques, show that our anonymization technique gives the most effective data sharing solution, with respect to computational cost and balancing between anonymity and data retention. Full article

(This article belongs to the Special Issue Privacy in Smart Health)

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18 pages, 1450 KiB

Open AccessArticle

Introducing Various Semantic Models for Amharic: Experimentation and Evaluation with Multiple Tasks and Datasets

by Seid Muhie Yimam, Abinew Ali Ayele, Gopalakrishnan Venkatesh, Ibrahim Gashaw and Chris Biemann

Future Internet 2021, 13(11), 275; https://doi.org/10.3390/fi13110275 - 27 Oct 2021

Cited by 21 | Viewed by 4502

Abstract

The availability of different pre-trained semantic models has enabled the quick development of machine learning components for downstream applications. However, even if texts are abundant for low-resource languages, there are very few semantic models publicly available. Most of the publicly available pre-trained models [...] Read more.

The availability of different pre-trained semantic models has enabled the quick development of machine learning components for downstream applications. However, even if texts are abundant for low-resource languages, there are very few semantic models publicly available. Most of the publicly available pre-trained models are usually built as a multilingual version of semantic models that will not fit well with the need for low-resource languages. We introduce different semantic models for Amharic, a morphologically complex Ethio-Semitic language. After we investigate the publicly available pre-trained semantic models, we fine-tune two pre-trained models and train seven new different models. The models include Word2Vec embeddings, distributional thesaurus (DT), BERT-like contextual embeddings, and DT embeddings obtained via network embedding algorithms. Moreover, we employ these models for different NLP tasks and study their impact. We find that newly-trained models perform better than pre-trained multilingual models. Furthermore, models based on contextual embeddings from FLAIR and RoBERTa perform better than word2Vec models for the NER and POS tagging tasks. DT-based network embeddings are suitable for the sentiment classification task. We publicly release all the semantic models, machine learning components, and several benchmark datasets such as NER, POS tagging, sentiment classification, as well as Amharic versions of WordSim353 and SimLex999. Full article

(This article belongs to the Special Issue Natural Language Engineering: Methods, Tasks and Applications)

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21 pages, 20332 KiB

Open AccessArticle

Software Design and Experimental Evaluation of a Reduced AES for IoT Applications

by Malik Qasaimeh, Raad S. Al-Qassas and Mohammad Ababneh

Future Internet 2021, 13(11), 273; https://doi.org/10.3390/fi13110273 - 27 Oct 2021

Cited by 8 | Viewed by 3049

Abstract

IoT devices include RFID tags, microprocessors, sensors, readers, and actuators. Their main characteristics are their limited resources and computing capabilities, which pose critical challenges to the reliability and security of their applications. Encryption is necessary for security when using these limited-resource devices, but [...] Read more.

IoT devices include RFID tags, microprocessors, sensors, readers, and actuators. Their main characteristics are their limited resources and computing capabilities, which pose critical challenges to the reliability and security of their applications. Encryption is necessary for security when using these limited-resource devices, but conventional cryptographic algorithms are too heavyweight and resource-demanding to run on IoT infrastructures. This paper presents a lightweight version of AES (called LAES), which provides competitive results in terms of randomness levels and processing time, operating on GF(2⁴). Detailed mathematical operations and proofs are presented concerning LAES rounds design fundamentals. The proposed LAES algorithm is evaluated based on its randomness, performance, and power consumption; it is then compared to other cryptographic algorithm variants, namely Present, Clefia, and AES. The design of the randomness and performance analysis is based on six measures developed with the help of the NIST test statistical suite of cryptographic applications. The performance and power consumption of LAES on a low-power, 8-bit microcontroller unit were evaluated using an Arduino Uno board. LAES was found to have competitive randomness levels, processing times, and power consumption compared to Present, Clefia, and AES. Full article

(This article belongs to the Collection Information Systems Security)

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14 pages, 658 KiB

Open AccessArticle

Securing Resource-Constrained IoT Nodes: Towards Intelligent Microcontroller-Based Attack Detection in Distributed Smart Applications

by Andrii Shalaginov and Muhammad Ajmal Azad

Future Internet 2021, 13(11), 272; https://doi.org/10.3390/fi13110272 - 27 Oct 2021

Cited by 7 | Viewed by 3309

Abstract

In recent years, the Internet of Things (IoT) devices have become an inseparable part of our lives. With the growing demand for Smart Applications, it becomes clear that IoT will bring regular automation and intelligent sensing to a new level thus improving quality [...] Read more.

In recent years, the Internet of Things (IoT) devices have become an inseparable part of our lives. With the growing demand for Smart Applications, it becomes clear that IoT will bring regular automation and intelligent sensing to a new level thus improving quality of life. The core component of the IoT ecosystem is data which exists in various forms and formats. The collected data is then later used to create context awareness and make meaningful decisions. Besides an undoubtedly large number of advantages from the usage of IoT, there exist numerous challenges attributed to the security of objects that cannot be neglected for uninterrupted services. The Mirai botnet attack demonstrated that the IoT system is susceptible to different forms of cyberattacks. While advanced data analytics and Machine Learning have proved efficiency in various applications of cybersecurity, those still have not been explored enough in the literature from the applicability perspective in the domain of resource-constrained IoT. Several architectures and frameworks have been proposed for defining the ways for analyzing the data, yet mostly investigating off-chip analysis. In this contribution, we show how an Artificial Neural Network model can be trained and deployed on trivial IoT nodes for detecting intelligent similarity-based network attacks. This article proposes a concept of the resource-constrained intelligent system as a part of the IoT infrastructure to be able to harden the cybersecurity on microcontrollers. This work will serve as a stepping stone for the application of Artificial Intelligence on devices with limited computing capabilities such as end-point IoT nodes. Full article

(This article belongs to the Collection Information Systems Security)

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45 pages, 12696 KiB

Open AccessArticle

HyDSMaaS: A Hybrid Communication Infrastructure with LoRaWAN and LoraMesh for the Demand Side Management as a Service

by Artur Felipe da Silva Veloso, José Valdemir Reis Júnior, Ricardo de Andrade Lira Rabelo and Jocines Dela-flora Silveira

Future Internet 2021, 13(11), 271; https://doi.org/10.3390/fi13110271 - 26 Oct 2021

Cited by 4 | Viewed by 3587

Abstract

Seeking to solve problems in the power electric system (PES) related to exacerbated and uncontrolled energy consumption by final consumers such as residences, condominiums, public buildings and industries, electric power companies (EPC) are increasingly seeking new information and communication technologies (ICTs) to transform [...] Read more.

Seeking to solve problems in the power electric system (PES) related to exacerbated and uncontrolled energy consumption by final consumers such as residences, condominiums, public buildings and industries, electric power companies (EPC) are increasingly seeking new information and communication technologies (ICTs) to transform traditional electric power distribution networks into smart grids (SG). With this implementation, PES will be able to remotely control electric power consumption as well as monitor data generated by smart meters (SM). However, Internet-of-Things (IoT) technologies will enable all this to happen quickly and at low cost, since they are low-cost devices that can be deployed quickly and at scale in these scenarios. With this in mind, this work aimed to study, propose, and implement a hybrid communication infrastructure with LoRaWAN and LoraMesh for the demand-side management as a service (HyDSMaaS) using IoT devices such as long range (LoRa) to provide an advanced metering infrastructure (AMI) capable of performing all these applications as a service offered by EPC to end consumers. Additionally, services such as demand-side management (DSMaaS) can be used in this infrastructure. From the preliminary results it was found that the LoRaWAN network achieved a range of up to 2.35 km distance and the LoRaMESH one of 600 m; thus, the latter is more suitable for scenarios where there is little interference and the SMs are at long distances, while the other is used for scenarios with greater agglomeration of nearby SMs. Considering the hybridized scenario between LoraWAN and LoRaMESH, it can be seen that the implementation possibilities increase, since its range was approximately 3 km considering only one hop, and it can reach 1023 devices present in a mesh network. Thus, it was possible to propose the actual implementation of LoRaWAN and LoRaMESH protocols as well as the hybridization of the two protocols for HyDSMaaS. Additionally, the results obtained are exclusively from Radioenge’s LoRa technology, which can be further improved in the case of using more powerful equipment. Full article

(This article belongs to the Special Issue Internet of Things (IoT) for Industry 4.0)

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24 pages, 823 KiB

Open AccessArticle

Dynamic Allocation of SDN Controllers in NFV-Based MEC for the Internet of Vehicles

by Rhodney Simões, Kelvin Dias and Ricardo Martins

Future Internet 2021, 13(11), 270; https://doi.org/10.3390/fi13110270 - 26 Oct 2021

Cited by 3 | Viewed by 2851

Abstract

The expected huge amount of connected cars and applications with varying Quality of Service (QoS) demands still depend on agile/flexible networking infrastructure to deal with dynamic service requests to the control plane, which may become a bottleneck for 5G and Beyond Software-Defined Network [...] Read more.

The expected huge amount of connected cars and applications with varying Quality of Service (QoS) demands still depend on agile/flexible networking infrastructure to deal with dynamic service requests to the control plane, which may become a bottleneck for 5G and Beyond Software-Defined Network (SDN) based Internet of Vehicles (IoV). At the heart of this issue is the need for an architecture and optimization mechanisms that benefit from cutting edge technologies while granting latency bounds in order to control and manage the dynamic nature of IoV. To this end, this article proposes an autonomic software-defined vehicular architecture grounded on the synergy of Multi-access Edge Computing (MEC) and Network Functions Virtualization (NFV) along with a heuristic approach and an exact model based on linear programming to efficiently optimize the dynamic resource allocation of SDN controllers, ensuring load balancing between controllers and employing reserve resources for tolerance in case of demand variation. The analyses carried out in this article consider: (a) to avoid waste of limited MEC resources, (b) to devise load balancing among controllers, (c) management complexity, and (d) to support scalability in dense IoV scenarios. The results show that the heuristic efficiently manages the environment even in highly dynamic and dense scenarios. Full article

(This article belongs to the Special Issue Software-Defined Vehicular Networking)

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14 pages, 3819 KiB

Open AccessArticle

COVIDNet: Implementing Parallel Architecture on Sound and Image for High Efficacy

by Manickam Murugappan, John Victor Joshua Thomas, Ugo Fiore, Yesudas Bevish Jinila and Subhashini Radhakrishnan

Future Internet 2021, 13(11), 269; https://doi.org/10.3390/fi13110269 - 26 Oct 2021

Cited by 4 | Viewed by 2445

Abstract

The present work relates to the implementation of core parallel architecture in a deep learning algorithm. At present, deep learning technology forms the main interdisciplinary basis of healthcare, hospital hygiene, biological and medicine. This work establishes a baseline range by training hyperparameter space, [...] Read more.

The present work relates to the implementation of core parallel architecture in a deep learning algorithm. At present, deep learning technology forms the main interdisciplinary basis of healthcare, hospital hygiene, biological and medicine. This work establishes a baseline range by training hyperparameter space, which could be support images, and sound with further develop a parallel architectural model using multiple inputs with and without the patient’s involvement. The chest X-ray images input could form the model architecture include variables for the number of nodes in each layer and dropout rate. Fourier transformation Mel-spectrogram images with the correct pixel range use to covert sound acceptance at the convolutional neural network in embarrassingly parallel sequences. COVIDNet the end user tool has to input a chest X-ray image and a cough audio file which could be a natural cough or a forced cough. Three binary classification models (COVID-19 CXR, non-COVID-19 CXR, COVID-19 cough) were trained. The COVID-19 CXR model classifies between healthy lungs and the COVID-19 model meanwhile the non-COVID-19 CXR model classifies between non-COVID-19 pneumonia and healthy lungs. The COVID-19 CXR model has an accuracy of 95% which was trained using 1681 COVID-19 positive images and 10,895 healthy lungs images, meanwhile, the non-COVID-19 CXR model has an accuracy of 91% which was trained using 7478 non-COVID-19 pneumonia positive images and 10,895 healthy lungs. The reason why all the models are binary classification is due to the lack of available data since medical image datasets are usually highly imbalanced and the cost of obtaining them are very pricey and time-consuming. Therefore, data augmentation was performed on the medical images datasets that were used. Effects of parallel architecture and optimization to improve on design were investigated. Full article

(This article belongs to the Collection Computer Vision, Deep Learning and Machine Learning with Applications)

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Future Internet, Volume 13, Issue 11 (November 2021) – 32 articles

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