Journal Description
Information
Information
is a scientific, peer-reviewed, open access journal of information science and technology, data, knowledge, and communication, and is published monthly online by MDPI. The International Society for Information Studies (IS4SI) is affiliated with Information and its members receive discounts on the article processing charges.
- 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, and other databases.
- Journal Rank: CiteScore - Q2 (Information Systems)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 21.8 days after submission; acceptance to publication is undertaken in 3.9 days (median values for papers published in this journal in the second 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
Federated Blockchain Learning at the Edge
Information 2023, 14(6), 318; https://doi.org/10.3390/info14060318 (registering DOI) - 30 May 2023
Abstract
Machine learning, particularly using neural networks, is now widely adopted in practice even with the IoT paradigm; however, training neural networks at the edge, on IoT devices, remains elusive, mainly due to computational requirements. Furthermore, effective training requires large quantities of data and
[...] Read more.
Machine learning, particularly using neural networks, is now widely adopted in practice even with the IoT paradigm; however, training neural networks at the edge, on IoT devices, remains elusive, mainly due to computational requirements. Furthermore, effective training requires large quantities of data and privacy concerns restrict accessible data. Therefore, in this paper, we propose a method leveraging a blockchain and federated learning to train neural networks at the edge effectively bypassing these issues and providing additional benefits such as distributing training across multiple devices. Federated learning trains networks without storing any data and aggregates multiple networks, trained on unique data, forming a global network via a centralized server. By leveraging the decentralized nature of a blockchain, this centralized server is replaced by a P2P network, removing the need for a trusted centralized server and enabling the learning process to be distributed across participating devices. Our results show that networks trained in such a manner have negligible differences in accuracy compared to traditionally trained networks on IoT devices and are less prone to overfitting. We conclude that not only is this a viable alternative to traditional paradigms but is an improvement that contains a wealth of benefits in an ecosystem such as a hospital.
Full article
(This article belongs to the Special Issue Pervasive Computing in IoT)
Open AccessArticle
An Intelligent Boosting and Decision-Tree-Regression-Based Score Prediction (BDTR-SP) Method in the Reform of Tertiary Education Teaching
Information 2023, 14(6), 317; https://doi.org/10.3390/info14060317 (registering DOI) - 30 May 2023
Abstract
The reform of tertiary education teaching promotes teachers to adjust timely teaching plans based on students’ learning feedback in order to improve teaching performance. Thefore, learning score prediction is a key issue in process of the reform of tertiary education teaching. With the
[...] Read more.
The reform of tertiary education teaching promotes teachers to adjust timely teaching plans based on students’ learning feedback in order to improve teaching performance. Thefore, learning score prediction is a key issue in process of the reform of tertiary education teaching. With the development of information and management technologies, a lot of teaching data are generated as the scale of online and offline education expands. However, a teacher or educator does not have a comprehensive dataset in practice, which challenges his/her ability to predict the students’ learning performance from the individual’s viewpoint. How to overcome the drawbacks of small samples is an open issue. To this end, it is desirable that an effective artificial intelligent tool is designed to help teachers or educators predict students’ scores well. We propose a boosting and decision-tree-regression-based score prediction (BDTR-SP) model, which relies on an ensemble learning structure with base learners of decision tree regression (DTR) to improve the prediction accuracy. Experiments on small samples are conducted to examine the important features that affect students’ scores. The results show that the proposed model has advantages over its peer in terms of prediction correctness. Moreover, the predicted results are consistent with the actual facts implied in the original dataset. The proposed BDTR-SP method aids teachers and students to predict students’ performance in the on-going courses in order to adjust the teaching and learning strategies, plans and practices in advance, enhancing the teaching and learning quality. Therefore, the integration of information technology and artificial intelligence into teaching and learning practices is able to push forward the reform of tertiary education teaching.
Full article
(This article belongs to the Special Issue Predictive Analytics and Data Science)
Open AccessArticle
Irradiance Non-Uniformity in LED Light Simulators
Information 2023, 14(6), 316; https://doi.org/10.3390/info14060316 (registering DOI) - 30 May 2023
Abstract
Photovoltaic (PV) cells are a technology of choice for providing power to self-sufficient Internet of Things (IoT) devices. These devices’ declining power demands can now be met even in indoor environments with low light intensity. Correspondingly, light simulation systems need to cover a
[...] Read more.
Photovoltaic (PV) cells are a technology of choice for providing power to self-sufficient Internet of Things (IoT) devices. These devices’ declining power demands can now be met even in indoor environments with low light intensity. Correspondingly, light simulation systems need to cover a wide spectrum of irradiance intensity to emulate a PV cell’s working conditions while meeting cost targets. In this paper, we propose a method for calculating the irradiance distribution for a given number and position of LED sources to meet irradiance and uniformity requirements in LED-based light simulators. In addition, we establish design guidelines for minimizing non-uniformity under specific constraints and utilize a function to evaluate the degree of non-uniformity and determine the optimal distance from the illuminated surface. We demonstrate that even with a small number of low-cost LED sources, high levels of irradiance can be achieved with bounded non-uniformities. The presented guidelines serve as a resource for designing tailored, low-cost light simulators that meet users’ area/intensity/uniformity specifications.
Full article
(This article belongs to the Special Issue Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM 2022))
Open AccessArticle
Exploiting Security Issues in Human Activity Recognition Systems (HARSs)
Information 2023, 14(6), 315; https://doi.org/10.3390/info14060315 (registering DOI) - 30 May 2023
Abstract
Human activity recognition systems (HARSs) are vital in a wide range of real-life applications and are a vibrant academic research area. Although they are adopted in many fields, such as the environment, agriculture, and healthcare and they are considered assistive technology, they seem
[...] Read more.
Human activity recognition systems (HARSs) are vital in a wide range of real-life applications and are a vibrant academic research area. Although they are adopted in many fields, such as the environment, agriculture, and healthcare and they are considered assistive technology, they seem to neglect the aspects of security and privacy. This problem occurs due to the pervasive nature of sensor-based HARSs. Sensors are devices with low power and computational capabilities, joining a machine learning application that lies in a dynamic and heterogeneous communication environment, and there is no generalized unified approach to evaluate their security/privacy, but rather only individual solutions. In this work, we studied HARSs in particular and tried to extend existing techniques for these systems considering the security/privacy of all participating components. Initially, in this work, we present the architecture of a real-life medical IoT application and the data flow across the participating entities. Then, we briefly review security and privacy issues and present possible vulnerabilities of each system layer. We introduce an architecture over the communication layer that offers mutual authentication, solving many security and privacy issues, particularly the man-in-the-middle attack (MitM). Relying on the proposed solutions, we manage to prevent unauthorized access to critical information by providing a trustworthy application.
Full article
(This article belongs to the Special Issue Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM 2022))
Open AccessArticle
METRIC—Multi-Eye to Robot Indoor Calibration Dataset
Information 2023, 14(6), 314; https://doi.org/10.3390/info14060314 - 29 May 2023
Abstract
Multi-camera systems are an effective solution for perceiving large areas or complex scenarios with many occlusions. In such a setup, an accurate camera network calibration is crucial in order to localize scene elements with respect to a single reference frame shared by all
[...] Read more.
Multi-camera systems are an effective solution for perceiving large areas or complex scenarios with many occlusions. In such a setup, an accurate camera network calibration is crucial in order to localize scene elements with respect to a single reference frame shared by all the viewpoints of the network. This is particularly important in applications such as object detection and people tracking. Multi-camera calibration is a critical requirement also in several robotics scenarios, particularly those involving a robotic workcell equipped with a manipulator surrounded by multiple sensors. Within this scenario, the robot-world hand-eye calibration is an additional crucial element for determining the exact position of each camera with respect to the robot, in order to provide information about the surrounding workspace directly to the manipulator. Despite the importance of the calibration process in the two scenarios outlined above, namely (i) a camera network, and (ii) a camera network with a robot, there is a lack of standard datasets available in the literature to evaluate and compare calibration methods. Moreover they are usually treated separately and tested on dedicated setups. In this paper, we propose a general standard dataset acquired in a robotic workcell where calibration methods can be evaluated in two use cases: camera network calibration and robot-world hand-eye calibration. The Multi-Eye To Robot Indoor Calibration (METRIC) dataset consists of over 10,000 synthetic and real images of ChAruCo and checkerboard patterns, each one rigidly attached to the robot end-effector, which was moved in front of four cameras surrounding the manipulator from different viewpoints during the image acquisition. The real images in the dataset includes several multi-view image sets captured by three different types of sensor networks: Microsoft Kinect V2, Intel RealSense Depth D455 and Intel RealSense Lidar L515, to evaluate their advantages and disadvantages for calibration. Furthermore, in order to accurately analyze the effect of camera-robot distance on calibration, we acquired a comprehensive synthetic dataset, with related ground truth, with three different camera network setups corresponding to three levels of calibration difficulty depending on the cell size. An additional contribution of this work is to provide a comprehensive evaluation of state-of-the-art calibration methods using our dataset, highlighting their strengths and weaknesses, in order to outline two benchmarks for the two aforementioned use cases.
Full article
(This article belongs to the Special Issue Computer Vision, Pattern Recognition and Machine Learning in Italy)
►▼
Show Figures

Figure 1
Open AccessArticle
Matrices Based on Descriptors for Analyzing the Interactions between Agents and Humans
Information 2023, 14(6), 313; https://doi.org/10.3390/info14060313 - 29 May 2023
Abstract
The design of agents interacting with human beings is becoming a crucial problem in many real-life applications. Different methods have been proposed in the research areas of human–computer interaction (HCI) and multi-agent systems (MAS) to model teams of participants (agents and humans). It
[...] Read more.
The design of agents interacting with human beings is becoming a crucial problem in many real-life applications. Different methods have been proposed in the research areas of human–computer interaction (HCI) and multi-agent systems (MAS) to model teams of participants (agents and humans). It is then necessary to build models analyzing their decisions when interacting, while taking into account the specificities of these interactions. This paper, therefore, aimed to propose an explicit model of such interactions based on game theory, taking into account, not only environmental characteristics (e.g., criticality), but also human characteristics (e.g., workload and experience level) for the intervention (or not) of agents, to help the latter. Game theory is a well-known approach to studying such social interactions between different participants. Existing works on the construction of game matrices required different ad hoc descriptors, depending on the application studied. Moreover, they generally focused on the interactions between agents, without considering human beings in the analysis. We show that these descriptors can be classified into two categories, related to their effect on the interactions. The set of descriptors to use is thus based on an explicit combination of all interactions between agents and humans (a weighted sum of 2-player matrices). We propose a general model for the construction of game matrices based on any number of participants and descriptors. It is then possible to determine using Nash equilibria whether agents decide (or not) to intervene during the tasks concerned. The model is also evaluated through the determination of the gains obtained by the different participants. Finally, we illustrate and validate the proposed model using a typical scenario (involving two agents and two humans), while describing the corresponding equilibria.
Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
►▼
Show Figures

Figure 1
Open AccessArticle
An Edge Device Framework in SEMAR IoT Application Server Platform
by
, , , , , , and
Information 2023, 14(6), 312; https://doi.org/10.3390/info14060312 - 29 May 2023
Abstract
Nowadays, the Internet of Things (IoT) has become widely used at various places and for various applications. To facilitate this trend, we have developed the IoT application server platform called SEMAR (Smart Environmental Monitoring and Analytical in Real-Time), which offers standard features
[...] Read more.
Nowadays, the Internet of Things (IoT) has become widely used at various places and for various applications. To facilitate this trend, we have developed the IoT application server platform called SEMAR (Smart Environmental Monitoring and Analytical in Real-Time), which offers standard features for collecting, displaying, and analyzing sensor data. An edge device is usually installed to connect sensors with the server, where the interface configuration, the data processing, the communication protocol, and the transmission interval need to be defined by the user. In this paper, we proposed an edge device framework for SEMAR to remotely optimize the edge device utilization with three phases. In the initialization phase, it automatically downloads the configuration file to the device through HTTP communications. In the service phase, it converts data from various sensors into the standard data format and sends it to the server periodically. In the update phase, it remotely updates the configuration through MQTT communications. For evaluations, we applied the proposal to the fingerprint-based indoor localization system (FILS15.4) and the data logging system. The results confirm the effectiveness in utilizing SEMAR to develop IoT application systems.
Full article
(This article belongs to the Special Issue Pervasive Computing in IoT)
►▼
Show Figures

Figure 1
Open AccessArticle
A Jigsaw Puzzle Solver-Based Attack on Image Encryption Using Vision Transformer for Privacy-Preserving DNNs
by
and
Information 2023, 14(6), 311; https://doi.org/10.3390/info14060311 - 29 May 2023
Abstract
In this paper, we propose a novel attack on image encryption for privacy-preserving deep neural networks (DNNs). Although several encryption schemes have been proposed for privacy-preserving DNNs, existing cipher-text-only attacks (COAs) have succeeded in restoring visual information from encrypted images. Image encryption using
[...] Read more.
In this paper, we propose a novel attack on image encryption for privacy-preserving deep neural networks (DNNs). Although several encryption schemes have been proposed for privacy-preserving DNNs, existing cipher-text-only attacks (COAs) have succeeded in restoring visual information from encrypted images. Image encryption using the Vision Transformer (ViT) is known to be robust against existing COAs due to the operations of block scrambling and pixel shuffling, which permute divided blocks and pixels in an encrypted image. However, the correlation between blocks in the encrypted image can still be exploited for reconstruction. Therefore, in this paper, a novel jigsaw puzzle solver-based attack that utilizes block correlation is proposed to restore visual information from encrypted images. In the experiments, we evaluated the security of image encryption for privacy-preserving deep neural networks using both conventional and proposed COAs. The experimental results demonstrate that the proposed attack is able to restore almost all visual information from images encrypted for being applied to ViTs.
Full article
(This article belongs to the Special Issue Addressing Privacy and Data Protection in New Technological Trends)
►▼
Show Figures

Figure 1
Open AccessArticle
A Robust Hybrid Deep Convolutional Neural Network for COVID-19 Disease Identification from Chest X-ray Images
by
, , , and
Information 2023, 14(6), 310; https://doi.org/10.3390/info14060310 - 29 May 2023
Abstract
The prompt and accurate identification of the causes of pneumonia is necessary to implement rapid treatment and preventative approaches, reduce the burden of infections, and develop more successful intervention strategies. There has been an increase in the number of new pneumonia cases and
[...] Read more.
The prompt and accurate identification of the causes of pneumonia is necessary to implement rapid treatment and preventative approaches, reduce the burden of infections, and develop more successful intervention strategies. There has been an increase in the number of new pneumonia cases and diseases known as acute respiratory distress syndrome (ARDS) as a direct consequence of the spread of COVID-19. Chest radiography has evolved to the point that it is now an indispensable diagnostic tool for COVID-19 infection pneumonia in hospitals. To fully exploit the technique, it is crucial to design a computer-aided diagnostic (CAD) system to assist doctors and other medical professionals in establishing an accurate and rapid diagnosis of pneumonia. This article presents a robust hybrid deep convolutional neural network (DCNN) for rapidly identifying three categories (normal, COVID-19 and pneumonia (viral or bacterial)) using X-ray image data sourced from the COVID-QU-Ex dataset. The proposed approach on the test set achieved a rate of 99.25% accuracy, 99.10% Kappa-score, 99.43% AUC, 99.24% F1-score, 99.25% recall, and 99.23% precision, respectively. The outcomes of the experiments demonstrate that the presented hybrid DCNN mechanism for identifying three categories utilising X-ray images is robust and effective.
Full article
(This article belongs to the Special Issue Artificial Intelligence and Big Data Applications)
►▼
Show Figures

Figure 1
Open AccessArticle
Fermatean Fuzzy-Based Personalized Prioritization of Barriers to IoT Adoption within the Clean Energy Context
by
, , , , and
Information 2023, 14(6), 309; https://doi.org/10.3390/info14060309 - 29 May 2023
Abstract
Globally, industries are focusing on green habits, with world leaders demanding net zero carbon; clean energy is considered an attractive and viable option. The Internet of things (IoT) is an emerging technology with potential opportunities in the clean energy domain for quality improvement
[...] Read more.
Globally, industries are focusing on green habits, with world leaders demanding net zero carbon; clean energy is considered an attractive and viable option. The Internet of things (IoT) is an emerging technology with potential opportunities in the clean energy domain for quality improvement in production and management. Earlier studies on IoTs show evidence that direct adoption of such digital technology is an ordeal and incurs adoption barriers that must be prioritized for effective management. Motivated by the claim, in this paper, the authors attempt to prioritize the diverse adoption barriers with the support of the newly proposed Fermatean fuzzy-based decision framework. Initially, qualitative rating information is collected via questionnaires on barriers and criteria from the circular economy (CE). Later, these are converted to Fermatean fuzzy numbers used by integrated approaches for decision processes. A regret scheme is put forward for determining CE criteria importance, and the barriers are prioritized by using a novel ranking algorithm that incorporates the WASPAS formulation and experts’ personal choices during rank estimation. The applicability of the developed framework is testified via a case example. Sensitivity analysis and comparison reveal the merits and limitations of the developed decision model. Results show that labor/workforce skill insufficiency, an ineffective framework for performance, a technology divide, insufficient legislation and control, and lack of time for training and skill practice are the top five barriers that hinder IoT adoption, based on the rating data. Additionally, the criteria such as cost cutting via a reuse scheme, resource circularity, emission control, and scaling profit with green habits are the top four criteria for their relative importance values. From these inferences, the respective authorities in the clean energy sector could effectively plan their strategies for addressing these barriers to promote IoT adoption in the clean energy sector.
Full article
(This article belongs to the Special Issue New Trend on Fuzzy Systems and Intelligent Decision Making Theory: A Themed Issue Dedicated to Dr. Ronald R. Yager)
►▼
Show Figures

Figure 1
Open AccessArticle
Empower Psychotherapy with mHealth Apps: The Design of “Safer”, an Emotion Regulation Application
Information 2023, 14(6), 308; https://doi.org/10.3390/info14060308 - 27 May 2023
Abstract
In the past decade, technological advancements in mental health care have resulted in new approaches and techniques. The proliferation of mobile apps and smartphones has significantly improved access to psychological self-help resources for individuals. In this paper, a narrative review offers a comprehensive
[...] Read more.
In the past decade, technological advancements in mental health care have resulted in new approaches and techniques. The proliferation of mobile apps and smartphones has significantly improved access to psychological self-help resources for individuals. In this paper, a narrative review offers a comprehensive overview of recent developments in mental health mobile apps, serving as a foundation to introduce the design and development of “Safer”. Safer is a mobile application that targets the transdiagnostic process of emotion dysregulation. The review outlines the theoretical framework and design of Safer, an mHealth app grounded in the Dialectical Behavior Therapy (DBT) model, aimed at fostering emotion regulation skills.
Full article
(This article belongs to the Special Issue eXtended Reality for Social Inclusion and Educational Purpose)
►▼
Show Figures

Figure 1
Open AccessArticle
An Intelligent Conversational Agent for the Legal Domain
Information 2023, 14(6), 307; https://doi.org/10.3390/info14060307 - 27 May 2023
Abstract
An intelligent conversational agent for the legal domain is an AI-powered system that can communicate with users in natural language and provide legal advice or assistance. In this paper, we present CREA2, an agent designed to process legal concepts and be able to
[...] Read more.
An intelligent conversational agent for the legal domain is an AI-powered system that can communicate with users in natural language and provide legal advice or assistance. In this paper, we present CREA2, an agent designed to process legal concepts and be able to guide users on legal matters. The conversational agent can help users navigate legal procedures, understand legal jargon, and provide recommendations for legal action. The agent can also give suggestions helpful in drafting legal documents, such as contracts, leases, and notices. Additionally, conversational agents can help reduce the workload of legal professionals by handling routine legal tasks. CREA2, in particular, will guide the user in resolving disputes between people residing within the European Union, proposing solutions in controversies between two or more people who are contending over assets in a divorce, an inheritance, or the division of a company. The conversational agent can later be accessed through various channels, including messaging platforms, websites, and mobile applications. This paper presents a retrieval system that evaluates the similarity between a user’s query and a given question. The system uses natural language processing (NLP) algorithms to interpret user input and associate responses by addressing the problem as a semantic search similar question retrieval. Although a common approach to question and answer (Q&A) retrieval is to create labelled Q&A pairs for training, we exploit an unsupervised information retrieval system in order to evaluate the similarity degree between a given query and a set of questions contained in the knowledge base. We used the recently proposed SBERT model for the evaluation of relevance. In the paper, we illustrate the effective design principles, the implemented details and the results of the conversational system and describe the experimental campaign carried out on it.
Full article
(This article belongs to the Collection Natural Language Processing and Applications: Challenges and Perspectives)
►▼
Show Figures

Figure 1
Open AccessArticle
Regularized Generalized Logistic Item Response Model
Information 2023, 14(6), 306; https://doi.org/10.3390/info14060306 - 26 May 2023
Abstract
Item response theory (IRT) models are factor models for dichotomous or polytomous variables (i.e., item responses). The symmetric logistic or probit link functions are most frequently utilized for modeling dichotomous or polytomous items. In this article, we propose an IRT model for dichotomous
[...] Read more.
Item response theory (IRT) models are factor models for dichotomous or polytomous variables (i.e., item responses). The symmetric logistic or probit link functions are most frequently utilized for modeling dichotomous or polytomous items. In this article, we propose an IRT model for dichotomous and polytomous items using the asymmetric generalistic logistic link function that covers a lot of symmetric and asymmetric link functions. Compared to IRT modeling based on the logistic or probit link function, the generalized logistic link function additionally estimates two parameters related to the asymmetry of the link function. To stabilize the estimation of item-specific asymmetry parameters, regularized estimation is employed. The usefulness of the proposed model is illustrated through simulations and empirical examples for dichotomous and polytomous item responses.
Full article
(This article belongs to the Special Issue Advances in Data and Network Sciences Applied to Computational Social Science)
►▼
Show Figures

Figure 1
Open AccessArticle
Customized AI Readers: An Adaptive Framework for Flexible Human Handwriting Recognition of Numerical Digits with OCR Methods
by
, , , , and
Information 2023, 14(6), 305; https://doi.org/10.3390/info14060305 - 26 May 2023
Abstract
Advanced artificial intelligence (AI) techniques have led to significant developments in optical character recognition (OCR) technologies. OCR applications, using AI techniques for transforming images of typed text, handwritten text, or other forms of text into machine-encoded text, provide a fair degree of accuracy
[...] Read more.
Advanced artificial intelligence (AI) techniques have led to significant developments in optical character recognition (OCR) technologies. OCR applications, using AI techniques for transforming images of typed text, handwritten text, or other forms of text into machine-encoded text, provide a fair degree of accuracy for general text. However, even after decades of intensive research, creating OCR with human-like abilities has remained evasive. One of the challenges has been that OCR models trained on general text do not perform well on localized or personalized handwritten text due to differences in the writing style of alphabets and digits. This study aims to discuss the steps needed to create an adaptive framework for OCR models, with the intent of exploring a reasonable method to customize an OCR solution for a unique dataset of English language numerical digits were developed for this study. We develop a digit recognizer by training our model on the MNIST dataset with a convolutional neural network and contrast it with multiple models trained on combinations of the MNIST and custom digits. Using our methods, we observed results comparable with the baseline and provided recommendations for improving OCR accuracy for localized or personalized handwritten text. This study also provides an alternative perspective to generating data using conventional methods, which can serve as a gold standard for custom data augmentation to help address the challenges of scarce data and data imbalance.
Full article
(This article belongs to the Special Issue Advances in Machine Learning and Intelligent Information Systems)
►▼
Show Figures

Figure 1
Open AccessArticle
Biparametric Q Rung Orthopair Fuzzy Entropy Measure for Multi Criteria Decision Making Problem
by
, , , , and
Information 2023, 14(6), 304; https://doi.org/10.3390/info14060304 - 26 May 2023
Abstract
►▼
Show Figures
In this study we propose a measure of the entropy of the norm (R, S) for q-row orthopair fuzzy sets (qROFS). The proposed entropy measure is validated both theoretically and practically to ensure validity. We also propose a simple methodology for the purpose
[...] Read more.
In this study we propose a measure of the entropy of the norm (R, S) for q-row orthopair fuzzy sets (qROFS). The proposed entropy measure is validated both theoretically and practically to ensure validity. We also propose a simple methodology for the purpose of solving a multi-criteria decision-analysis problems using the introduced entropy measure. This method takes into account different circumstances of criteria weights, such as unknown weights, as well as other cases when the weights are not fully known. Finally, a demonstration with numerical examples for the proposed entropy has been provided to show how to apply the novel methodologies.
Full article

Figure 1
Open AccessArticle
Multilingual Text Summarization for German Texts Using Transformer Models
Information 2023, 14(6), 303; https://doi.org/10.3390/info14060303 - 25 May 2023
Abstract
The tremendous increase in documents available on the Web has turned finding the relevant pieces of information into a challenging, tedious, and time-consuming activity. Text summarization is an important natural language processing (NLP) task used to reduce the reading requirements of text. Automatic
[...] Read more.
The tremendous increase in documents available on the Web has turned finding the relevant pieces of information into a challenging, tedious, and time-consuming activity. Text summarization is an important natural language processing (NLP) task used to reduce the reading requirements of text. Automatic text summarization is an NLP task that consists of creating a shorter version of a text document which is coherent and maintains the most relevant information of the original text. In recent years, automatic text summarization has received significant attention, as it can be applied to a wide range of applications such as the extraction of highlights from scientific papers or the generation of summaries of news articles. In this research project, we are focused mainly on abstractive text summarization that extracts the most important contents from a text in a rephrased form. The main purpose of this project is to summarize texts in German. Unfortunately, most pretrained models are only available for English. We therefore focused on the German BERT multilingual model and the BART monolingual model for English, with a consideration of translation possibilities. As the source of the experiment setup, took the German Wikipedia article dataset and compared how well the multilingual model performed for German text summarization when compared to using machine-translated text summaries from monolingual English language models. We used the ROUGE-1 metric to analyze the quality of the text summarization.
Full article
(This article belongs to the Collection Natural Language Processing and Applications: Challenges and Perspectives)
►▼
Show Figures

Figure 1
Open AccessArticle
Philosophy and Meanings of the Information Entropy Analysis of Road Safety: Case Study of Russian Cities
Information 2023, 14(6), 302; https://doi.org/10.3390/info14060302 - 24 May 2023
Abstract
This article is devoted to the study of the entropic orderliness of road safety systems of various dimensionalities. The author’s methodology for quantitative assessment of the quality of the road safety systems is based on the use of information entropy analysis, the essence
[...] Read more.
This article is devoted to the study of the entropic orderliness of road safety systems of various dimensionalities. The author’s methodology for quantitative assessment of the quality of the road safety systems is based on the use of information entropy analysis, the essence of which is to assess the significance (or “weights”) of various information-technological stages of the road traffic accident rate formation process. The main emphasis in this paper is on the philosophical interpretation of the results of entropic evaluation of the orderliness of urban road safety systems. The article aimed to philosophically understand the reasons for the diversity in the results of assessing the entropy of road safety (RS) in Russian cities. Within the framework of this goal, the results of the analysis of the state of the issue, ideological approaches and methods for assessing the relative entropy of urban road safety systems were presented. The study was based on analyzing statistics that characterize the processes of the formation of road traffic accidents in Russian cities classified into three groups based on population size. The experimental results obtained were explained from the point of view of human psychology. Rather, results were explained from the perspective of human psychology. The final results of the study once again illustrated the objectivity of Hegel’s dialectical laws and, perhaps, once again shattered illusions about the possibility of achieving high levels of road safety in cities by building rigid systems to regulate the actions of traffic participants. In the author’s opinion, the results of the presented philosophical analysis will be useful to managers specializing in the management of complex systems (not only transport but also other fields) to comprehend the contradictions of the complex nature of humans and the paradoxes of their behavior when their freedom of action is restricted through external control.
Full article
(This article belongs to the Section Information Theory and Methodology)
►▼
Show Figures

Figure 1
Open AccessArticle
A Study of Machine Learning Regression Techniques for Non-Contact SpO2 Estimation from Infrared Motion-Magnified Facial Video
Information 2023, 14(6), 301; https://doi.org/10.3390/info14060301 - 23 May 2023
Abstract
This work explores the use of infrared low-cost cameras for monitoring peripheral oxygen saturation (SpO2), a vital sign that is particularly important for individuals with fragile health, such as the elderly. The development of contactless SpO2 monitoring utilizing RGB cameras
[...] Read more.
This work explores the use of infrared low-cost cameras for monitoring peripheral oxygen saturation (SpO2), a vital sign that is particularly important for individuals with fragile health, such as the elderly. The development of contactless SpO2 monitoring utilizing RGB cameras has already proven successful. This study utilizes the Eulerian Video Magnification (EVM) technique to enhance minor variations in skin pixel intensity in particular facial regions. More specifically, the emphasis in this study is in the utilization of infrared cameras, in order to explore the possibility of contactless SpO2 monitoring under low-light or night-time conditions. Many different methods were employed for regression. A study of machine learning regression methods was performed, including a Generalized Additive Model (GAM) and an Extra Trees Regressor, based on 12 novel features extracted from the extracted amplified photoplethysmography (PPG) signal. Deep learning methods were also explored, including a 3D Convolution Neural Network (CNN) and a Video Vision Transformer (ViViT) architecture on the amplified forehead/cheeks video. The estimated SpO2 values of the best performing method reach a low root mean squared error of 1.331 and an score of 0.465 that fall within the acceptable range for these applications.
Full article
(This article belongs to the Section Biomedical Information and Health)
►▼
Show Figures

Figure 1
Open AccessArticle
A Web-Based Docker Image Assistant Generation Tool for User-PC Computing System
Information 2023, 14(6), 300; https://doi.org/10.3390/info14060300 (registering DOI) - 23 May 2023
Abstract
Currently, we are developing the user-PC computing (UPC) system based on the master-worker model as a scalable, low-cost, and high-performance computing platform. To run various application programs on personal computers (PCs) with different environments for workers, it adopts Docker technology to bundle
[...] Read more.
Currently, we are developing the user-PC computing (UPC) system based on the master-worker model as a scalable, low-cost, and high-performance computing platform. To run various application programs on personal computers (PCs) with different environments for workers, it adopts Docker technology to bundle every necessary software as one image file. Unfortunately, the Docker file/image are manually generated through multiple steps by a user, which can be the bottleneck. In this paper, we present a web-based Docker image assistant generation (DIAG) tool in the UPC system to assist or reduce these process steps. It adopts Angular JavaScript for offering user interfaces, PHP Laravel for handling logic using RestAPI, MySQL database for storing data, and Shell scripting for speedily running the whole program. In addition, the worker-side code modification function is implemented so that a user can modify the source code of the running job and update the Docker image at a worker to speed up them. For evaluations, we collected 30 Docker files and 10 OpenFOAM jobs through reverse processing from Docker images in Github and generated the Docker images using the tool. Moreover, we modified source codes for network simulations and generated the Docker images in a worker five times. The results confirmed the validity of the proposal.
Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
►▼
Show Figures

Figure 1
Open AccessArticle
Reinforcement Learning-Based Hybrid Multi-Objective Optimization Algorithm Design
by
and
Information 2023, 14(5), 299; https://doi.org/10.3390/info14050299 - 22 May 2023
Abstract
The multi-objective optimization (MOO) of complex systems remains a challenging task in engineering domains. The methodological approach of applying MOO algorithms to simulation-enabled models has established itself as a standard. Despite increasing in computational power, the effectiveness and efficiency of such algorithms, i.e.,
[...] Read more.
The multi-objective optimization (MOO) of complex systems remains a challenging task in engineering domains. The methodological approach of applying MOO algorithms to simulation-enabled models has established itself as a standard. Despite increasing in computational power, the effectiveness and efficiency of such algorithms, i.e., their ability to identify as many Pareto-optimal solutions as possible with as few simulation samples as possible, plays a decisive role. However, the question of which class of MOO algorithms is most effective or efficient with respect to which class of problems has not yet been resolved. To tackle this performance problem, hybrid optimization algorithms that combine multiple elementary search strategies have been proposed. Despite their potential, no systematic approach for selecting and combining elementary Pareto search strategies has yet been suggested. In this paper, we propose an approach for designing hybrid MOO algorithms that uses reinforcement learning (RL) techniques to train an intelligent agent for dynamically selecting and combining elementary MOO search strategies. We present both the fundamental RL-Based Hybrid MOO (RLhybMOO) methodology and an exemplary implementation applied to mathematical test functions. The results indicate a significant performance gain of intelligent agents over elementary and static hybrid search strategies, highlighting their ability to effectively and efficiently select algorithms.
Full article
(This article belongs to the Special Issue Intelligent Agent and Multi-Agent System)
►▼
Show Figures

Figure 1

Journal Menu
► ▼ Journal Menu-
- Information Home
- Aims & Scope
- Editorial Board
- Reviewer Board
- Topical Advisory Panel
- Instructions for Authors
- Special Issues
- Topics
- Sections & Collections
- Article Processing Charge
- Indexing & Archiving
- Editor’s Choice Articles
- Most Cited & Viewed
- Journal Statistics
- Journal History
- Journal Awards
- Society Collaborations
- Conferences
- Editorial Office
Journal Browser
► ▼ Journal BrowserHighly Accessed Articles
Latest Books
E-Mail Alert
News
Topics
Topic in
Education Sciences, Future Internet, Information, J. Intell., Sustainability
Advances in Online and Distance Learning
Topic Editors: Neil Gordon, Han ReichgeltDeadline: 31 July 2023
Topic in
Applied Sciences, Electronics, Informatics, Information, Software
Software Engineering and Applications
Topic Editors: Sanjay Misra, Robertas Damaševičius, Bharti SuriDeadline: 31 October 2023
Topic in
Algorithms, Axioms, Information, Mathematics, Symmetry
Fuzzy Number, Fuzzy Difference, Fuzzy Differential: Theory and Applications
Topic Editors: Changyou Wang, Dong Qiu, Yonghong ShenDeadline: 20 December 2023
Topic in
AI, Algorithms, Applied Sciences, Information, Mathematics
Advances in Artificial Neural Networks
Topic Editors: Krzysztof Ejsmont, Aamer Bilal Asghar, Yong Wang, Rodolfo HaberDeadline: 31 December 2023

Conferences
Special Issues
Special Issue in
Information
Recommendation Algorithms and Web Mining
Guest Editor: Ida MeleDeadline: 31 May 2023
Special Issue in
Information
Big Data, IoT and Cloud Computing
Guest Editor: Habtamu AbieDeadline: 14 June 2023
Special Issue in
Information
Security and Privacy in IoT Systems (SPIoTS)
Guest Editors: Habtamu Abie, Stefan Poslad, John SoldatosDeadline: 30 June 2023
Special Issue in
Information
Models for Blockchain Systems: Analysis and Simulation
Guest Editors: Sabina Rossi, Andrea Marin, Marco BernardoDeadline: 15 July 2023
Topical Collections
Topical Collection in
Information
Natural Language Processing and Applications: Challenges and Perspectives
Collection Editor: Diego Reforgiato Recupero
Topical Collection in
Information
Knowledge Graphs for Search and Recommendation
Collection Editors: Pierpaolo Basile, Annalina Caputo
Topical Collection in
Information
Augmented Reality Technologies, Systems and Applications
Collection Editors: Ramon Fabregat, Jorge Bacca-Acosta, N.D. Duque-Mendez
Topical Collection in
Information
Pervasive Intelligent Data Systems
Collection Editors: Filipe Portela, Manuel Filipe Santos, Kolomvatsos Kostas