Journal Description
Future Internet
Future Internet
is a scholarly, peer-reviewed, open access journal on Internet technologies and the information society, published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), Ei Compendex, dblp, Inspec, and other databases.
- Journal Rank: CiteScore - Q2 (Computer Networks and Communications)
- Rapid Publication: manuscripts are peer-reviewed and a first decision provided to authors approximately 12.6 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the first half of 2022).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Latest Articles
Distributed Big Data Storage Infrastructure for Biomedical Research Featuring High-Performance and Rich-Features
Future Internet 2022, 14(10), 273; https://doi.org/10.3390/fi14100273 (registering DOI) - 24 Sep 2022
Abstract
The biomedical field entered the era of “big data” years ago, and a lot of software is being developed to tackle the analysis problems brought on by big data. However, very few programs focus on providing a solid foundation for file systems of
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The biomedical field entered the era of “big data” years ago, and a lot of software is being developed to tackle the analysis problems brought on by big data. However, very few programs focus on providing a solid foundation for file systems of biomedical big data. Since file systems are a key prerequisite for efficient big data utilization, the absence of specialized biomedical big data file systems makes it difficult to optimize storage, accelerate analysis, and enrich functionality, resulting in inefficiency. Here we present F3BFS, a functional, fundamental, and future-oriented distributed file system, specially designed for various kinds of biomedical data. F3BFS makes it possible to boost existing software’s performance without modifying its main algorithms by transmitting raw datasets from generic file systems. Further, F3BFS has various built-in features to help researchers manage biology datasets more efficiently and productively, including metadata management, fuzzy search, automatic backup, transparent compression, etc.
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(This article belongs to the Special Issue Software Engineering and Data Science II)
Open AccessArticle
Author Identification from Literary Articles with Visual Features: A Case Study with Bangla Documents
by
, , , , , and
Future Internet 2022, 14(10), 272; https://doi.org/10.3390/fi14100272 (registering DOI) - 23 Sep 2022
Abstract
Author identification is an important aspect of literary analysis, studied in natural language processing (NLP). It aids identify the most probable author of articles, news texts or social media comments and tweets, for example. It can be applied to other domains such as
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Author identification is an important aspect of literary analysis, studied in natural language processing (NLP). It aids identify the most probable author of articles, news texts or social media comments and tweets, for example. It can be applied to other domains such as criminal and civil cases, cybersecurity, forensics, identification of plagiarizer, and many more. An automated system in this context can thus be very beneficial for society. In this paper, we propose a convolutional neural network (CNN)-based author identification system from literary articles. This system uses visual features along with a five-layer convolutional neural network for the identification of authors. The prime motivation behind this approach was the feasibility to identify distinct writing styles through a visualization of the writing patterns. Experiments were performed on 1200 articles from 50 authors achieving a maximum accuracy of 93.58%. Furthermore, to see how the system performed on different volumes of data, the experiments were performed on partitions of the dataset. The system outperformed standard handcrafted feature-based techniques as well as established works on publicly available datasets.
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(This article belongs to the Special Issue Deep Learning and Natural Language Processing)
Open AccessArticle
An Enhanced ELECTRE II Method for Multi-Attribute Ontology Ranking with Z-Numbers and Probabilistic Linguistic Term Set
Future Internet 2022, 14(10), 271; https://doi.org/10.3390/fi14100271 - 21 Sep 2022
Abstract
The high number of ontologies available on the web to date makes it increasingly difficult to select appropriate ontologies for reuse. Many studies have attempted to provide support for ontology selection and ranking; however, the existing studies provide support for ontology ranking from
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The high number of ontologies available on the web to date makes it increasingly difficult to select appropriate ontologies for reuse. Many studies have attempted to provide support for ontology selection and ranking; however, the existing studies provide support for ontology ranking from an objective perspective as opposed to a subjective perspective. They do not take into account the qualitative aspects of ontologies. Furthermore, the existing methods have a limited focus on group environments. In this paper, a multi-criteria decision-making approach is presented for ontology ranking with the development of an enhanced model combining the ELECTRE II model with the Z-Probabilistic Linguistic Term Set (ZPLTS). The ZPLTS-ELECTRE II model enables decision-makers to model ontology ranking problems using both numerical and linguistic data. Furthermore, the newly proposed model provides support for ontology ranking in group settings, with an emphasis on modeling the differing levels of credibility of decision-makers using the ZPLTS, which allows decision-makers to not only specify their opinion but also specify their level of credibility. The model was applied to rank a set of mental health ontologies obtained from the BioPortal repository. The results showed that the method was able to rank the ontologies successfully. The results were further compared with the traditional ELECTRE II and the PLTS ELECTRE II methods, displaying superior modeling capabilities. This paper demonstrated the effectiveness of the newly proposed ZPLTS-ELECTRE II model for ontology ranking in a real-world context, but the method is not constrained to the ontology ranking domain; rather, it may be applied to other real-world decision problems as well.
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(This article belongs to the Section Techno-Social Smart Systems)
Open AccessArticle
Analysis and Correlation between a Non-Invasive Sensor Network System in the Room and the Improvement of Sleep Quality
Future Internet 2022, 14(10), 270; https://doi.org/10.3390/fi14100270 - 20 Sep 2022
Abstract
Good sleep quality is essential in human life due to its impact on health. Currently, technology has focused on providing specific features for quality sleep monitoring in people. This work represents a contribution to state of the art on non-invasive technologies that can
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Good sleep quality is essential in human life due to its impact on health. Currently, technology has focused on providing specific features for quality sleep monitoring in people. This work represents a contribution to state of the art on non-invasive technologies that can help improve the quality of people’s sleep at a low cost. We reviewed the sleep quality of a group of people by analyzing their good and bad sleeping habits. We take that information to feed a proposed algorithm for a non-invasive sensor network in the person’s room for monitoring factors that help them fall asleep. We analyze vital signs and health conditions in order to be able to relate these parameters to the person’s way of sleeping. We help people get valuable information about their sleep with technology to live a healthy life, and we get about a 15% improvement in sleep quality. Finally, we compare the implementations given by the network with wearables to show the improvement in the behavior of the person’s sleep.
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(This article belongs to the Special Issue New Technologies and Smart Solutions in IoT-Based Personalized Healthcare Applications)
Open AccessArticle
Low Power Blockchained E-Vote Platform for University Environment
Future Internet 2022, 14(9), 269; https://doi.org/10.3390/fi14090269 - 19 Sep 2022
Abstract
With the onset of the COVID-19 pandemic and the succession of its waves, the transmission of this disease and the number of deaths caused by it have been increasing. Despite the various vaccines, the COVID-19 virus is still contagious and dangerous for affected
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With the onset of the COVID-19 pandemic and the succession of its waves, the transmission of this disease and the number of deaths caused by it have been increasing. Despite the various vaccines, the COVID-19 virus is still contagious and dangerous for affected people. One of the remedies to this is precaution, and particularly social distancing. In the same vein, this paper proposes a remote voting system, which has to be secure, anonymous, irreversible, accessible, and simple to use. It therefore allows voters to have the possibility to vote for their candidate without having to perform the operation on site. This system will be used for university elections and particularly for student elections. We propose a platform based on a decentralized system. This system will use two blockchains communicating with each other: the public Ethereum blockchain and the private Quorum blockchain. The private blockchain will be institution-specific. All these blockchains send the necessary data to the public blockchain which manages different data related to the universities and the ministry. This system enables using encrypted data with the SHA-256 algorithm to have both security and information security. Motivated by the high energy consumption of blockchain and by the performance improvements in low-power, a test is performed on a low-power embedded platform Raspberry PI4 showing the possibility to use the Blockchain with limited resources.
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(This article belongs to the Collection Innovative People-Centered Solutions Applied to Industries, Cities and Societies)
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Open AccessArticle
Joint Scalable Video Coding and Transcoding Solutions for Fog-Computing-Assisted DASH Video Applications
Future Internet 2022, 14(9), 268; https://doi.org/10.3390/fi14090268 - 17 Sep 2022
Abstract
Video streaming solutions have increased their importance in the last decade, enabling video on demand (VoD) services. Among several innovative services, 5G and Beyond 5G (B5G) systems consider the possibility of providing VoD-based solutions for surveillance applications, citizen information and e-tourism applications, to
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Video streaming solutions have increased their importance in the last decade, enabling video on demand (VoD) services. Among several innovative services, 5G and Beyond 5G (B5G) systems consider the possibility of providing VoD-based solutions for surveillance applications, citizen information and e-tourism applications, to name a few. Although the majority of the implemented solutions resort to a centralized cloud-based approach, the interest in edge/fog-based approaches is increasing. Fog-based VoD services result in fulfilling the stringent low-latency requirement of 5G and B5G networks. In the following, by resorting to the Dynamic Adaptive Streaming over HTTP (DASH) technique, we design a video-segment deployment algorithm for streaming services in a fog computing environment. In particular, by exploiting the inherent adaptation of the DASH approach, we embed in the system a joint transcoding and scalable video coding (SVC) approach able to deploy at run-time the video segments upon the user’s request. With this in mind, two algorithms have been developed aiming at maximizing the marginal gain with respect to a pre-defined delay threshold and enabling video quality downgrade for faster video deployment. Numerical results demonstrate that by effectively mapping the video segments, it is possible to minimize the streaming latency while maximising the users’ target video quality.
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(This article belongs to the Special Issue Key Enabling Technologies for Beyond 5G Networks)
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Open AccessArticle
An Efficient Blockchain Transaction Retrieval System
Future Internet 2022, 14(9), 267; https://doi.org/10.3390/fi14090267 - 15 Sep 2022
Abstract
In the era of the digital economy, blockchain has developed well in various fields, such as finance and digital copyright, due to its unique decentralization and traceability characteristics. However, blockchain gradually exposes the storage problem, and the current blockchain stores the block data
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In the era of the digital economy, blockchain has developed well in various fields, such as finance and digital copyright, due to its unique decentralization and traceability characteristics. However, blockchain gradually exposes the storage problem, and the current blockchain stores the block data in third-party storage systems to reduce the node storage pressure. The new blockchain storage method brings the blockchain transaction retrieval problem. The problem is that when unable to locate the block containing this transaction, the user must fetch the entire blockchain ledger data from the third-party storage system, resulting in huge communication overhead. For this problem, we exploit the semi-structured data in the blockchain and extract the universal blockchain transaction characteristics, such as account address and time. Then we establish a blockchain transaction retrieval system. Responding to the lacking efficient retrieval data structure, we propose a scalable secondary search data structure BB+ tree for account address and introduce the I2B+ tree for time. Finally, we analyze the proposed scheme’s performance through experiments. The experiment results prove that our system is superior to the existing methods in single-feature retrieval, concurrent retrieval, and multi-feature hybrid retrieval. The retrieval time under single feature retrieval is reduced by 40.54%, and the retrieval time is decreased by 43.16% under the multi-feature hybrid retrieval. It has better stability in different block sizes and concurrent retrieval scales.
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(This article belongs to the Special Issue Trends of Data Science and Knowledge Discovery)
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Open AccessArticle
Assistance System for the Teaching of Natural Numbers to Preschool Children with the Use of Artificial Intelligence Algorithms
Future Internet 2022, 14(9), 266; https://doi.org/10.3390/fi14090266 - 15 Sep 2022
Abstract
This research was aimed at designing an image recognition system that can help increase children’s interest in learning natural numbers between 0 and 9. The research method used was qualitative descriptive, observing early childhood learning in a face-to-face education model, especially in the
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This research was aimed at designing an image recognition system that can help increase children’s interest in learning natural numbers between 0 and 9. The research method used was qualitative descriptive, observing early childhood learning in a face-to-face education model, especially in the learning of numbers, with additional data from literature studies. For the development of the system, the cascade method was used, consisting of three stages: identification of the population, design of the artificial intelligence architecture, and implementation of the recognition system. The method of the system sought to replicate a mechanic that simulates a game, whereby the child trains the artificial intelligence algorithm such that it recognizes the numbers that the child draws on a blackboard. The system is expected to help increase the ability of children in their interest to learn numbers and identify the meaning of quantities to help improve teaching success with a fun and engaging teaching method for children. The implementation of learning in this system is expected to make it easier for children to learn to write, read, and conceive the quantities of numbers, in addition to exploring their potential, creativity, and interest in learning, with the use of technologies.
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(This article belongs to the Special Issue Artificial Neural Networks for Educational Data Mining in Higher Education)
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Open AccessReview
Approaches and Challenges in Internet of Robotic Things
Future Internet 2022, 14(9), 265; https://doi.org/10.3390/fi14090265 - 14 Sep 2022
Abstract
The Internet of robotic things (IoRT) is the combination of different technologies including cloud computing, robots, Internet of things (IoT), artificial intelligence (AI), and machine learning (ML). IoRT plays a major role in manufacturing, healthcare, security, and transport. IoRT can speed up human
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The Internet of robotic things (IoRT) is the combination of different technologies including cloud computing, robots, Internet of things (IoT), artificial intelligence (AI), and machine learning (ML). IoRT plays a major role in manufacturing, healthcare, security, and transport. IoRT can speed up human development by a very significant percentage. IoRT allows robots to transmit and receive data to and from other devices and users. In this paper, IoRT is reviewed in terms of the related techniques, architectures, and abilities. Consequently, the related research challenges are presented. IoRT architectures are vital in the design of robotic systems and robotic things. The existing 3–7-tier IoRT architectures are studied. Subsequently, a detailed IoRT architecture is proposed. Robotic technologies provide the means to increase the performance and capabilities of the user, product, or process. However, robotic technologies are vulnerable to attacks on data security. Trust-based and encryption-based mechanisms can be used for secure communication among robotic things. A security method is recommended to provide a secure and trustworthy data-sharing mechanism in IoRT. Significant security challenges are also discussed. Several known attacks on ad hoc networks are illustrated. Threat models ensure integrity confidentiality and availability of the data. In a network, trust models are used to boost a system’s security. Trust models and IoRT networks play a key role in obtaining a steady and nonvulnerable configuration in the network. In IoRT, remote server access results in remote software updates of robotic things. To study navigation strategies, navigation using fuzzy logic, probabilistic roadmap algorithms, laser scan matching algorithms, heuristic functions, bumper events, and vision-based navigation techniques are considered. Using the given research challenges, future researchers can get contemporary ideas of IoRT implementation in the real world.
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(This article belongs to the Special Issue Security and Privacy in Blockchains and the IoT II)
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Open AccessArticle
A VPN Performances Analysis of Constrained Hardware Open Source Infrastructure Deploy in IoT Environment
Future Internet 2022, 14(9), 264; https://doi.org/10.3390/fi14090264 - 13 Sep 2022
Abstract
Virtual private network (VPN) represents an HW/SW infrastructure that implements private and confidential communication channels that usually travel through the Internet. VPN is currently one of the most reliable technologies to achieve this goal, also because being a consolidated technology, it is possible
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Virtual private network (VPN) represents an HW/SW infrastructure that implements private and confidential communication channels that usually travel through the Internet. VPN is currently one of the most reliable technologies to achieve this goal, also because being a consolidated technology, it is possible to apply appropriate patches to remedy any security holes. In this paper we analyze the performances of open source firmware OpenWrt 21.x compared with a server-side operating system (Debian 11 x64) and Mikrotik 7.x, also virtualized, and different types of clients (Windows 10/11, iOS 15, Android 11, OpenWrt 21.x, Debian 11 x64 and Mikrotik 7.x), observing the performance of the network according to the current implementation of the various protocols and algorithms of VPN tunnel examined on what are the most recent HW and SW for deployment in outdoor locations with poor network connectivity. Specifically, operating systems provide different performance metric values for various combinations of configuration variables. The first pursued goal is to find the algorithms to guarantee a data transmission/encryption ratio as efficiently as possible. The second goal is to research the algorithms capable of guaranteeing the widest spectrum of compatibility with the current infrastructures that support VPN technology, to obtain a connection system secure for geographically scattered IoT networks spread over difficult-to-manage areas such as suburban or rural environments. The third goal is to be able to use open firmware on constrained routers that provide compatibility with different VPN protocols.
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(This article belongs to the Special Issue Security and Privacy in Blockchains and the IoT II)
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Open AccessArticle
Trade-offs between Risk and Operational Cost in SDN Failure Recovery Plan
Future Internet 2022, 14(9), 263; https://doi.org/10.3390/fi14090263 - 13 Sep 2022
Abstract
We consider the problem of SDN flow optimization in the presence of a dynamic probabilistic link failures model. We introduce a metric for path risk, which can change dynamically as network conditions and failure probabilities change. As these probabilities change, the end-to-end path
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We consider the problem of SDN flow optimization in the presence of a dynamic probabilistic link failures model. We introduce a metric for path risk, which can change dynamically as network conditions and failure probabilities change. As these probabilities change, the end-to-end path survivability probability may drop, i.e., its risk may rise. The main objective is to reroute at-risk end-to-end flows with the minimum number of flow operation so that a fast flow recovery is guaranteed. We provide various formulations for optimizing network risk versus operational costs and examine the trade-offs in flow recovery and the connections between operational cost, path risk, and path survival probability. We present our suboptimal dynamic flow restoration methods and evaluate their effectiveness against the Lagrangian relaxation approach. Our results show a significant improvement in operational cost against a shortest-path approach.
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(This article belongs to the Section Network Virtualization and Edge/Fog Computing)
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Open AccessArticle
Retrieving Adversarial Cliques in Cognitive Communities: A New Conceptual Framework for Scientific Knowledge Graphs
Future Internet 2022, 14(9), 262; https://doi.org/10.3390/fi14090262 - 07 Sep 2022
Abstract
The variety and diversity of published content are currently expanding in all fields of scholarly communication. Yet, scientific knowledge graphs (SKG) provide only poor images of the varied directions of alternative scientific choices, and in particular scientific controversies, which are not currently identified
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The variety and diversity of published content are currently expanding in all fields of scholarly communication. Yet, scientific knowledge graphs (SKG) provide only poor images of the varied directions of alternative scientific choices, and in particular scientific controversies, which are not currently identified and interpreted. We propose to use the rich variety of knowledge present in search histories to represent cliques modeling the main interpretable practices of information retrieval issued from the same “cognitive community”, identified by their use of keywords and by the search experience of the users sharing the same research question. Modeling typical cliques belonging to the same cognitive community is achieved through a new conceptual framework, based on user profiles, namely a bipartite geometric scientific knowledge graph, SKG GRAPHYP. Further studies of interpretation will test differences of documentary profiles and their meaning in various possible contexts which studies on “disagreements in scientific literature” have outlined. This final adjusted version of GRAPHYP optimizes the modeling of “Manifold Subnetworks of Cliques in Cognitive Communities” (MSCCC), captured from previous user experience in the same search domain. Cliques are built from graph grids of three parameters outlining the manifold of search experiences: mass of users; intensity of uses of items; and attention, identified as a ratio of “feature augmentation” by literature on information retrieval, its mean value allows calculation of an observed “steady” value of the user/item ratio or, conversely, a documentary behavior “deviating” from this mean value. An illustration of our approach is supplied in a positive first test, which stimulates further work on modeling subnetworks of users in search experience, that could help identify the varied alternative documentary sources of information retrieval, and in particular the scientific controversies and scholarly disputes.
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(This article belongs to the Special Issue Information Retrieval on the Semantic Web)
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Open AccessArticle
A Fairness Index Based on Rate Variance for Downlink Non-Orthogonal Multiple Access System
Future Internet 2022, 14(9), 261; https://doi.org/10.3390/fi14090261 - 31 Aug 2022
Abstract
Aiming at the resource allocation problem of a non-orthogonal multiple access (NOMA) system, a fairness index based on sample variance of users’ transmission rates is proposed, which has a fixed range and high sensitivity. Based on the proposed fairness index, the fairness-constrained power
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Aiming at the resource allocation problem of a non-orthogonal multiple access (NOMA) system, a fairness index based on sample variance of users’ transmission rates is proposed, which has a fixed range and high sensitivity. Based on the proposed fairness index, the fairness-constrained power allocation problem in NOMA system is studied; the problem is decoupled into the intra cluster power allocation problem and the inter cluster power allocation problem. The nonconvex optimization problem is solved by the continuous convex approximation (SCA) method, and an intra and inter cluster power iterative allocation algorithm with fairness constrained is proposed to maximize the total throughput. Simulation results show that the proposed algorithm can take into account intra cluster, inter cluster, and system fairness, and maximize the system throughput on the premise of fairness.
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(This article belongs to the Special Issue 6G Wireless Communication Systems: Applications, Opportunities and Challenges II)
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Open AccessArticle
Use of Data Augmentation Techniques in Detection of Antisocial Behavior Using Deep Learning Methods
Future Internet 2022, 14(9), 260; https://doi.org/10.3390/fi14090260 - 31 Aug 2022
Abstract
The work presented in this paper focuses on the use of data augmentation techniques applied in the domain of the detection of antisocial behavior. Data augmentation is a frequently used approach to overcome issues related to the lack of data or problems related
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The work presented in this paper focuses on the use of data augmentation techniques applied in the domain of the detection of antisocial behavior. Data augmentation is a frequently used approach to overcome issues related to the lack of data or problems related to imbalanced classes. Such techniques are used to generate artificial data samples used to improve the volume of the training set or to balance the target distribution. In the antisocial behavior detection domain, we frequently face both issues, the lack of quality labeled data as well as class imbalance. As the majority of the data in this domain is textual, we must consider augmentation methods suitable for NLP tasks. Easy data augmentation (EDA) represents a group of such methods utilizing simple text transformations to create the new, artificial samples. Our main motivation is to explore EDA techniques’ usability on the selected tasks from the antisocial behavior detection domain. We focus on the class imbalance problem and apply EDA techniques to two problems: fake news and toxic comments classification. In both cases, we train the convolutional neural networks classifier and compare its performance on the original and EDA-extended datasets. EDA techniques prove to be very task-dependent, with certain limitations resulting from the data they are applied on. The model’s performance on the extended toxic comments dataset did improve only marginally, gaining only 0.01 improvement in the F1 metric when applying only a subset of EDA methods. EDA techniques in this case were not suitable enough to handle texts written in more informal language. On the other hand, on the fake news dataset, the performance was improved more significantly, boosting the F1 score by 0.1. Improvement was most significant in the prediction of the minor class, where F1 improved from 0.67 to 0.86.
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(This article belongs to the Special Issue Trends of Data Science and Knowledge Discovery)
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Open AccessSystematic Review
Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis
Future Internet 2022, 14(9), 259; https://doi.org/10.3390/fi14090259 - 30 Aug 2022
Abstract
With the effects of climate change such as increasing heat, higher rainfall, and more recurrent extreme weather events including storms and floods, a unique approach to studying the effects of climatic elements on groundwater level variations is required. These unique approaches will help
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With the effects of climate change such as increasing heat, higher rainfall, and more recurrent extreme weather events including storms and floods, a unique approach to studying the effects of climatic elements on groundwater level variations is required. These unique approaches will help people make better decisions. Researchers and stakeholders can attain these goals if they become familiar with current machine learning and mathematical model approaches to predicting groundwater level changes. However, descriptions of machine learning and mathematical model approaches for forecasting groundwater level changes are lacking. This study picked 117 papers from the Scopus scholarly database to address this knowledge gap. In a systematic review, the publications were examined using quantitative and qualitative approaches, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was chosen as the reporting format. Machine learning and mathematical model techniques have made significant contributions to predicting groundwater level changes, according to the study. However, the domain is skewed because machine learning has been more popular in recent years, with random forest (RF) methods dominating, followed by the methods of support vector machine (SVM) and artificial neural network (ANN). Machine learning ensembles have also been found to help with aspects of computational complexity, such as performance and training times. Furthermore, compared to mathematical model techniques, machine learning approaches achieve higher accuracies, according to our research. As a result, it is advised that academics employ new machine learning techniques while also considering mathematical model approaches to predicting groundwater level changes.
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(This article belongs to the Special Issue Machine Learning Perspective in the Convolutional Neural Network Era)
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Open AccessArticle
Facial Expression Recognition Using Dual Path Feature Fusion and Stacked Attention
Future Internet 2022, 14(9), 258; https://doi.org/10.3390/fi14090258 - 30 Aug 2022
Abstract
Facial Expression Recognition (FER) can achieve an understanding of the emotional changes of a specific target group. The relatively small dataset related to facial expression recognition and the lack of a high accuracy of expression recognition are both a challenge for researchers. In
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Facial Expression Recognition (FER) can achieve an understanding of the emotional changes of a specific target group. The relatively small dataset related to facial expression recognition and the lack of a high accuracy of expression recognition are both a challenge for researchers. In recent years, with the rapid development of computer technology, especially the great progress of deep learning, more and more convolutional neural networks have been developed for FER research. Most of the convolutional neural performances are not good enough when dealing with the problems of overfitting from too-small datasets and noise, due to expression-independent intra-class differences. In this paper, we propose a Dual Path Stacked Attention Network (DPSAN) to better cope with the above challenges. Firstly, the features of key regions in faces are extracted using segmentation, and irrelevant regions are ignored, which effectively suppresses intra-class differences. Secondly, by providing the global image and segmented local image regions as training data for the integrated dual path model, the overfitting problem of the deep network due to a lack of data can be effectively mitigated. Finally, this paper also designs a stacked attention module to weight the fused feature maps according to the importance of each part for expression recognition. For the cropping scheme, this paper chooses to adopt a cropping method based on the fixed four regions of the face image, to segment out the key image regions and to ignore the irrelevant regions, so as to improve the efficiency of the algorithm computation. The experimental results on the public datasets, CK+ and FERPLUS, demonstrate the effectiveness of DPSAN, and its accuracy reaches the level of current state-of-the-art methods on both CK+ and FERPLUS, with 93.2% and 87.63% accuracy on the CK+ dataset and FERPLUS dataset, respectively.
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(This article belongs to the Special Issue Developments of Computer Vision and Image Processing: Methodologies and Applications)
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Open AccessArticle
A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance
Future Internet 2022, 14(9), 257; https://doi.org/10.3390/fi14090257 - 29 Aug 2022
Abstract
There is no well-defined utility function for existing multipath TCP algorithms. Therefore, network utility maximization (NUM) for MPTCP is a complex undertaking. To resolve this, we develop a novel condition under which Kelly’s NUM mechanism may be used to explicitly compute the equilibrium.
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There is no well-defined utility function for existing multipath TCP algorithms. Therefore, network utility maximization (NUM) for MPTCP is a complex undertaking. To resolve this, we develop a novel condition under which Kelly’s NUM mechanism may be used to explicitly compute the equilibrium. We accomplish this by defining a new utility function for MPTCP by employing Tullock’s rent-seeking paradigm from game theory. We investigate the convergence of no-regret learning in the underlying network games with continuous actions. Based on our understanding of the design space, we propose an original MPTCP algorithm that generalizes existing algorithms and strikes a good balance among the important properties. We implemented this algorithm in the Linux kernel, and we evaluated its performance experimentally.
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(This article belongs to the Special Issue 5G Wireless Communication Networks)
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Open AccessArticle
Intelligent Reflecting Surface-Aided Device-to-Device Communication: A Deep Reinforcement Learning Approach
by
and
Future Internet 2022, 14(9), 256; https://doi.org/10.3390/fi14090256 - 29 Aug 2022
Abstract
Recently, the growing demand of various emerging applications in the realms of sixth-generation (6G) wireless networks has made the term internet of Things (IoT) very popular. Device-to-device (D2D) communication has emerged as one of the significant enablers for the 6G-based IoT network. Recently,
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Recently, the growing demand of various emerging applications in the realms of sixth-generation (6G) wireless networks has made the term internet of Things (IoT) very popular. Device-to-device (D2D) communication has emerged as one of the significant enablers for the 6G-based IoT network. Recently, the intelligent reflecting surface (IRS) has been considered as a hardware-efficient innovative scheme for future wireless networks due to its ability to mitigate propagation-induced impairments and to realize a smart radio environment. Such an IRS-assisted D2D underlay cellular network is investigated in this paper. Our aim is to maximize the network’s spectrum efficiency (SE) by jointly optimizing the transmit power of both the cellular users (CUs) and the D2D pairs, the resource reuse indicators, and the IRS reflection coefficients. Instead of using traditional optimization solution schemes to solve this mixed integer nonlinear optimization problem, a reinforcement learning (RL) approach is used in this paper. The IRS-assisted D2D communication network is structured by the Markov Decision Process (MDP) in the RL framework. First, a Q-learning-based solution is studied. Then, to make a scalable solution with large dimension state and action spaces, a deep Q-learning-based solution scheme using experience replay is proposed. Lastly, an actor-critic framework based on the deep deterministic policy gradient (DDPG) scheme is proposed to learn the optimal policy of the constructed optimization problem considering continuous-valued state and action spaces. Simulation outcomes reveal that the proposed RL-based solution schemes can provide significant SE enhancements compared to the existing optimization schemes.
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(This article belongs to the Special Issue AI, Machine Learning and Data Analytics for Wireless Communications)
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Open AccessArticle
Smart Classroom Teaching Strategy to Enhance Higher Order Thinking Skills (HOTS)—An Agile Approach for Education 4.0
Future Internet 2022, 14(9), 255; https://doi.org/10.3390/fi14090255 - 28 Aug 2022
Abstract
The development of Industry 4.0 revolutionising the concept of automation and digitisation in an organisation poses a huge challenge in employee knowledge and skills to cope with the huge leap from Industry 3.0. The high-level digitisation of an organisation requires the workforce to
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The development of Industry 4.0 revolutionising the concept of automation and digitisation in an organisation poses a huge challenge in employee knowledge and skills to cope with the huge leap from Industry 3.0. The high-level digitisation of an organisation requires the workforce to possess higher order thinking skills (HOTS) for the changing job roles matching the rapid technological advancements. The Education 4.0 framework is aimed at supporting the Industry 4.0 skills requirement not only in digital technologies but more towards soft skill development such as collaboration and lifelong learning. However, the education sector is also facing challenges in its transition from Education 3.0 to Education 4.0. The main purpose of the paper is to propose an Agile approach for developing smart classroom teaching strategies that foster employee adaptability with the new learning paradigm of upskilling in line with Industry 4.0. By adopting an exploratory research methodology, the pilot study investigates the implementation of the proposed Agile approach in a higher education setting for graduates to achieve HOTS using smart classroom teaching strategies. This study uses learning theories such as experiential learning in smart classroom environments to enhance students’ HOTS individually as well as collaboratively in an Agile iterative manner. This is the first empirical study carried out for graduates specialising in the Business Analytics skillset required for Industry 4.0. The findings of the pilot study show promising results that pave the way for further exploration and pedagogical insights in this research direction.
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(This article belongs to the Section Smart System Infrastructure and Applications)
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Open AccessArticle
Framework for Video Steganography Using Integer Wavelet Transform and JPEG Compression
Future Internet 2022, 14(9), 254; https://doi.org/10.3390/fi14090254 - 25 Aug 2022
Abstract
In today’s world of computers everyone is communicating their personal information through the web. So, the security of personal information is the main concern from the research point of view. Steganography can be used for the security purpose of personal information. Storing and
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In today’s world of computers everyone is communicating their personal information through the web. So, the security of personal information is the main concern from the research point of view. Steganography can be used for the security purpose of personal information. Storing and forwarding of embedded personal information specifically in public places is gaining more attention day by day. In this research work, the Integer Wavelet Transform technique along with JPEG (Joint Photograph Expert Group) compression is proposed to overcome some of the issues associated with steganography techniques. Video cover files and JPEG compression improve concealing capacity because of their intrinsic properties. Integer Wavelet Transform is used to improve the imperceptibility and robustness of the proposed technique. The Imperceptibility of the proposed work is analyzed through evaluation parameters such as PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), SSIM (Structure Similarity Metric), and CC (Correlation Coefficient). Robustness is validated through some image processing attacks. Complexity is calculated in terms of concealing and retrieval time along with the amount of secret information hidden.
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(This article belongs to the Special Issue Distributed Systems and Artificial Intelligence)
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