Special Issue "Feature Papers in Information in 2023"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: 31 December 2023 | Viewed by 24007

Special Issue Editors

School of Computer Science and Software Engineering, University of Wollongong, Northfields Avenue, Wollongong, NSW 2522, Australia
Interests: cryptography; computer security; design of signature schemes
Special Issues, Collections and Topics in MDPI journals
Department of Industrial Design, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
Interests: information systems; information applications; cyber-physical systems; social computing; interaction design; human computer interaction; internet of things
Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Campus de Montegancedo S/N, Boadilla del Monte, 28660 Madrid, Spain
Interests: multi-attribute utility theory, group decision making; preference quantification; metaheuristics; simulation, risk analysis and management
Special Issues, Collections and Topics in MDPI journals
School of Engineering, Edith Cowan University, Joondalup, WA 6027, Australia
Interests: signal processing; artificial intelligence; electronics; sensors; applied mathematics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As Editor-in-Chief and Editorial Board Members of Information, we are pleased to announce the Special Issue entitled "Feature Papers in Information in 2022". This Special Issue will be a collection of high-quality papers from Editorial Board Members and leading researchers invited by the Editorial Office. Both original research articles and comprehensive review papers are welcome. All topics related to information processing in various fields and applications are welcome.

Prof. Dr. Willy Susilo
Dr. Jun Hu
Prof. Dr. Antonio Jiménez-Martín
Prof. Dr. Zahir M. Hussain
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (20 papers)

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Research

Article
Effects of Generative Chatbots in Higher Education
Information 2023, 14(9), 492; https://doi.org/10.3390/info14090492 - 07 Sep 2023
Viewed by 856
Abstract
Learning technologies often do not meet the university requirements for learner engagement via interactivity and real-time feedback. In addition to the challenge of providing personalized learning experiences for students, these technologies can increase the workload of instructors due to the maintenance and updates [...] Read more.
Learning technologies often do not meet the university requirements for learner engagement via interactivity and real-time feedback. In addition to the challenge of providing personalized learning experiences for students, these technologies can increase the workload of instructors due to the maintenance and updates required to keep the courses up-to-date. Intelligent chatbots based on generative artificial intelligence (AI) technology can help overcome these disadvantages by transforming pedagogical activities and guiding both students and instructors interactively. In this study, we explore and compare the main characteristics of existing educational chatbots. Then, we propose a new theoretical framework for blended learning with intelligent chatbots integration enabling students to interact online and instructors to create and manage their courses using generative AI tools. The advantages of the proposed framework are as follows: (1) it provides a comprehensive understanding of the transformative potential of AI chatbots in education and facilitates their effective implementation; (2) it offers a holistic methodology to enhance the overall educational experience; and (3) it unifies the applications of intelligent chatbots in teaching–learning activities within universities. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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Article
Developing a Serious Game for Rail Services: Improving Passenger Information During Disruption (PIDD)
Information 2023, 14(8), 464; https://doi.org/10.3390/info14080464 - 17 Aug 2023
Viewed by 461
Abstract
Managing passenger information during disruption (PIDD) is a significant factor in running effective and quick-to-recover rail operations. Disruptions are unpredictable, and their timely resolution is ultimately dependent on the expert knowledge of experienced frontline staff. The development of frontline employees by their employers [...] Read more.
Managing passenger information during disruption (PIDD) is a significant factor in running effective and quick-to-recover rail operations. Disruptions are unpredictable, and their timely resolution is ultimately dependent on the expert knowledge of experienced frontline staff. The development of frontline employees by their employers usually takes the form of practice reviews and ‘on-the-job’ learning, while academic education majors on theoretical approaches and classroom-based teaching. This paper reports on a novel industry-funded project that has developed a serious game (the ‘Rail Disruption Game’) that combines theory and practice to better manage PIDD for frontline staff in a UK train operating company (TOC). It defines challenges and the development method for the Rail Disruption Game; it also incorporates developer and user feedback. This paper provides insight into how to design, make and deploy a serious game as part of a gamified management process. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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Article
InterviewBot: Real-Time End-to-End Dialogue System for Interviewing Students for College Admission
Information 2023, 14(8), 460; https://doi.org/10.3390/info14080460 - 15 Aug 2023
Viewed by 567
Abstract
We present the InterviewBot, which dynamically integrates conversation history and customized topics into a coherent embedding space to conduct 10 min hybrid-domain (open and closed) conversations with foreign students applying to U.S. colleges to assess their academic and cultural readiness. To build a [...] Read more.
We present the InterviewBot, which dynamically integrates conversation history and customized topics into a coherent embedding space to conduct 10 min hybrid-domain (open and closed) conversations with foreign students applying to U.S. colleges to assess their academic and cultural readiness. To build a neural-based end-to-end dialogue model, 7361 audio recordings of human-to-human interviews are automatically transcribed, where 440 are manually corrected for finetuning and evaluation. To overcome the input/output size limit of a transformer-based encoder–decoder model, two new methods are proposed, context attention and topic storing, allowing the model to make relevant and consistent interactions. Our final model is tested both statistically by comparing its responses to the interview data and dynamically by inviting professional interviewers and various students to interact with it in real-time, finding it highly satisfactory in fluency and context awareness. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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Article
Unveiling Key Themes and Establishing a Hierarchical Taxonomy of Disaster-Related Tweets: A Text Mining Approach for Enhanced Emergency Management Planning
Information 2023, 14(7), 385; https://doi.org/10.3390/info14070385 - 07 Jul 2023
Viewed by 882
Abstract
Effectively harnessing the power of social media data for disaster management requires sophisticated analysis methods and frameworks. This research focuses on understanding the contextual information present in social media posts during disasters and developing a taxonomy to effectively categorize and classify the diverse [...] Read more.
Effectively harnessing the power of social media data for disaster management requires sophisticated analysis methods and frameworks. This research focuses on understanding the contextual information present in social media posts during disasters and developing a taxonomy to effectively categorize and classify the diverse range of topics discussed. First, the existing literature on social media analysis in disaster management is explored, highlighting the limitations and gaps in current methodologies. Second, a dataset comprising real-time social media posts related to various disasters is collected and preprocessed to ensure data quality and reliability. Third, three well-established topic modeling techniques, namely Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), and Non-Negative Matrix Factorization (NMF), are employed to extract and analyze the latent topics and themes present in the social media data. The contributions of this research lie in the development of a taxonomy that effectively categorizes and classifies disaster-related social media data, the identification of key latent topics and themes, and the extraction of valuable insights to support and enhance emergency management efforts. Overall, the findings of this research have the potential to transform the way emergency management and response are conducted by harnessing the power of social media data. By incorporating these insights into decision-making processes, emergency managers can make more informed and strategic choices, resulting in more efficient and effective emergency response strategies. This, in turn, leads to improved outcomes, better utilization of resources, and ultimately, the ability to save lives and mitigate the impacts of disasters. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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Article
INSUS: Indoor Navigation System Using Unity and Smartphone for User Ambulation Assistance
Information 2023, 14(7), 359; https://doi.org/10.3390/info14070359 - 24 Jun 2023
Viewed by 1042
Abstract
Currently, outdoor navigation systems have widely been used around the world on smartphones. They rely on GPS (Global Positioning System). However, indoor navigation systems are still under development due to the complex structure of indoor environments, including multiple floors, many rooms, steps, and [...] Read more.
Currently, outdoor navigation systems have widely been used around the world on smartphones. They rely on GPS (Global Positioning System). However, indoor navigation systems are still under development due to the complex structure of indoor environments, including multiple floors, many rooms, steps, and elevators. In this paper, we present the design and implementation of the Indoor Navigation System using Unity and Smartphone (INSUS). INSUS shows the arrow of the moving direction on the camera view based on a smartphone’s augmented reality (AR) technology. To trace the user location, it utilizes the Simultaneous Localization and Mapping (SLAM) technique with a gyroscope and a camera in a smartphone to track users’ movements inside a building after initializing the current location by the QR code. Unity is introduced to obtain the 3D information of the target indoor environment for Visual SLAM. The data are stored in the IoT application server called SEMAR for visualizations. We implement a prototype system of INSUS inside buildings in two universities. We found that scanning QR codes with the smartphone perpendicular in angle between 60 and 100 achieves the highest QR code detection accuracy. We also found that the phone’s tilt angles influence the navigation success rate, with 90 to 100 tilt angles giving better navigation success compared to lower tilt angles. INSUS also proved to be a robust navigation system, evidenced by near identical navigation success rate results in navigation scenarios with or without disturbance. Furthermore, based on the questionnaire responses from the respondents, it was generally found that INSUS received positive feedback and there is support to improve the system. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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Article
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
Viewed by 659
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)
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Article
A Web-Based Docker Image Assistant Generation Tool for User-PC Computing System
Information 2023, 14(6), 300; https://doi.org/10.3390/info14060300 - 23 May 2023
Viewed by 1081
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)
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Article
Simulated Autonomous Driving Using Reinforcement Learning: A Comparative Study on Unity’s ML-Agents Framework
Information 2023, 14(5), 290; https://doi.org/10.3390/info14050290 - 14 May 2023
Viewed by 1734
Abstract
Advancements in artificial intelligence are leading researchers to find use cases that were not as straightforward to solve in the past. The use case of simulated autonomous driving has been known as a notoriously difficult task to automate, but advancements in the field [...] Read more.
Advancements in artificial intelligence are leading researchers to find use cases that were not as straightforward to solve in the past. The use case of simulated autonomous driving has been known as a notoriously difficult task to automate, but advancements in the field of reinforcement learning have made it possible to reach satisfactory results. In this paper, we explore the use of the Unity ML-Agents toolkit to train intelligent agents to navigate a racing track in a simulated environment using RL algorithms. The paper compares the performance of several different RL algorithms and configurations on the task of training kart agents to successfully traverse a racing track and identifies the most effective approach for training kart agents to navigate a racing track and avoid obstacles in that track. The best results, value loss of 0.0013 and a cumulative reward of 0.761, were yielded using the Proximal Policy Optimization algorithm. After successfully choosing a model and algorithm that can traverse the track with ease, different objects were added to the track and another model (which used behavioral cloning as a pre-training option) was trained to avoid such obstacles. The aforementioned model resulted in a value loss of 0.001 and a cumulative reward of 0.068, proving that behavioral cloning can help achieve satisfactory results where the in game agents are able to avoid obstacles more efficiently and complete the track with human-like performance, allowing for a deployment of intelligent agents in racing simulators. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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Article
Hemodynamic and Electrophysiological Biomarkers of Interpersonal Tuning during Interoceptive Synchronization
Information 2023, 14(5), 289; https://doi.org/10.3390/info14050289 - 13 May 2023
Viewed by 961
Abstract
This research explored the influence of interoception and social frame on the coherence of inter-brain electrophysiological (EEG) and hemodynamic (collected by functional Near Infrared Spectroscopy, fNIRS) functional connectivity during a motor synchronization task. Fourteen dyads executed a motor synchronization task with the presence [...] Read more.
This research explored the influence of interoception and social frame on the coherence of inter-brain electrophysiological (EEG) and hemodynamic (collected by functional Near Infrared Spectroscopy, fNIRS) functional connectivity during a motor synchronization task. Fourteen dyads executed a motor synchronization task with the presence and absence of interoceptive focus. Moreover, the motor task was socially or not-socially framed by enhancing the shared intentionality. During the experiment, delta, theta, alpha, and beta frequency bands, and oxygenated and de-oxygenated hemoglobin (O2Hb and HHb) were collected through an EEG-fNIRS hyperscanning paradigm. Inter-brain coherence indices were computed for the two neurophysiological signals and then they were correlated to explore the reciprocal coherence of the functional connectivity EEG-fNIRS in the dyads. Findings showed significant higher correlational values between delta and O2Hb, theta and O2Hb, and alpha and O2Hb for the left hemisphere in the focus compared to the no focus condition and to the right hemisphere (both during focus and no focus condition). Additionally, greater correlational values between delta and O2Hb, and theta and O2Hb were observed in the left hemisphere for the focus condition when the task was socially compared to non-socially framed. This study showed that the focus on the breath and shared intentionality activate coherently the same left frontal areas in dyads performing a joint motor task. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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Article
Delay Indices for Train Punctuality
Information 2023, 14(5), 269; https://doi.org/10.3390/info14050269 - 01 May 2023
Viewed by 1338
Abstract
Indicators of expected quality of service in public contracts are often based on some kind of “punctuality”, usually defined in terms of the percentage of trains arriving at the final destination (and/or at intermediate significant stops) within a given delay. Passengers, however, tend [...] Read more.
Indicators of expected quality of service in public contracts are often based on some kind of “punctuality”, usually defined in terms of the percentage of trains arriving at the final destination (and/or at intermediate significant stops) within a given delay. Passengers, however, tend to use the word “punctuality” with a more general meaning, mostly as a synonym for expected delay at their own destination, and especially in case of commuters are much less tolerant of even smaller delays than train operators would normally allow. In particular, measuring the delay only at the final destination is perceived as largely inadequate, leading to underestimation of the actual percentage of late trains, and in turn undermining passengers’ trust in official performance statistics. In this paper, we take the passengers’ perspective, introducing a family of delay indices called D-indices aimed at capturing the overall performance of a train “as a whole”, taking into account both the delays at the sampling points and the mutual location and order of such sampling points. In this paper, all indicators have the physical dimension of time in order to be easily replaceable to other delay measures. We first present typical approaches and definitions of punctuality in the literature, then introduce D-indices while exploring their features, pros and cons, and relevant properties. We validate and discuss our approach by comparing this model with existing approaches both theoretically and by comparison with selected datasets consisting of about one hundred trains transcribed over the last three years. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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Article
Modeling Chronic Pain Experiences from Online Reports Using the Reddit Reports of Chronic Pain Dataset
Information 2023, 14(4), 237; https://doi.org/10.3390/info14040237 - 12 Apr 2023
Viewed by 1336
Abstract
Reported experiences of chronic pain may convey qualities relevant to the exploration of this private and subjective experience. We propose this exploration by means of the Reddit Reports of Chronic Pain (RRCP) dataset. We define and validate the RRCP for a set of [...] Read more.
Reported experiences of chronic pain may convey qualities relevant to the exploration of this private and subjective experience. We propose this exploration by means of the Reddit Reports of Chronic Pain (RRCP) dataset. We define and validate the RRCP for a set of subreddits related to chronic pain, identify the main concerns discussed in each subreddit, model each subreddit according to their main concerns, and compare subreddit models. The RRCP dataset comprises 86,537 submissions from 12 subreddits related to chronic pain (each related to one pathological background). Each RRCP subreddit was found to have various main concerns. Some of these concerns are shared between multiple subreddits (e.g., the subreddit Sciatica semantically entails the subreddit backpain in their various concerns, but not the other way around), whilst some concerns are exclusive to specific subreddits (e.g., Interstitialcystitis and CrohnsDisease). Our analysis details each of these concerns and their (dis)similarity relations. Although limited by the intrinsic qualities of the Reddit platform, to the best of our knowledge, this is the first research work attempting to model the linguistic expression of various chronic pain-inducing pathologies and comparing these models to identify and quantify the similarities and differences between the corresponding emergent, chronic pain experiences. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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Article
Probabilistic Forecasting of Residential Energy Consumption Based on SWT-QRTCN-ADSC-NLSTM Model
Information 2023, 14(4), 231; https://doi.org/10.3390/info14040231 - 08 Apr 2023
Viewed by 996
Abstract
Residential electricity consumption forecasting plays a crucial role in the rational allocation of resources reducing energy waste and enhancing the grid-connected operation of power systems. Probabilistic forecasting can provide more comprehensive information for the decision-making and dispatching process by quantifying the uncertainty of [...] Read more.
Residential electricity consumption forecasting plays a crucial role in the rational allocation of resources reducing energy waste and enhancing the grid-connected operation of power systems. Probabilistic forecasting can provide more comprehensive information for the decision-making and dispatching process by quantifying the uncertainty of electricity load. In this study, we propose a method based on stationary wavelet transform (SWT), quantile regression (QR), Bidirectional nested long short-term memory (BiNLSTM), and Depthwise separable convolution (DSC) combined with attention mechanism for electricity consumption probability prediction methods. First, the data sequence is decomposed using SWT to reduce the complexity of the sequence; then, the combined neural network model with attention is used to obtain the prediction values under different quantile conditions. Finally, the probability density curve of electricity consumption is obtained by combining kernel density estimation (KDE). The model was tested using historical demand-side data from five UK households to achieve energy consumption predictions 5 min in advance. It is demonstrated that the model can achieve both reliable probabilistic prediction and accurate deterministic prediction. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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Article
SEMKIS-DSL: A Domain-Specific Language to Support Requirements Engineering of Datasets and Neural Network Recognition
Information 2023, 14(4), 213; https://doi.org/10.3390/info14040213 - 01 Apr 2023
Cited by 2 | Viewed by 1042
Abstract
Neural network (NN) components are being increasingly incorporated into software systems. Neural network properties are determined by their architecture, as well as the training and testing datasets used. The engineering of datasets and neural networks is a challenging task that requires methods and [...] Read more.
Neural network (NN) components are being increasingly incorporated into software systems. Neural network properties are determined by their architecture, as well as the training and testing datasets used. The engineering of datasets and neural networks is a challenging task that requires methods and tools to satisfy customers’ expectations. The lack of tools that support requirements specification languages makes it difficult for engineers to describe dataset and neural network recognition skill requirements. Existing approaches often rely on traditional ad hoc approaches, without precise requirement specifications for data selection criteria, to build these datasets. Moreover, these approaches do not focus on the requirements of the neural network’s expected recognition skills. We aim to overcome this issue by defining a domain-specific language that precisely specifies dataset requirements and expected recognition skills after training for an NN-based system. In this paper, we present a textual domain-specific language (DSL) called SEMKIS-DSL (Software Engineering Methodology for the Knowledge management of Intelligent Systems) that is designed to support software engineers in specifying the requirements and recognition skills of neural networks. This DSL is proposed in the context of our general SEMKIS development process for neural network engineering. We illustrate the DSL’s concepts using a running example that focuses on the recognition of handwritten digits. We show some requirements and recognition skills specifications and demonstrate how our DSL improves neural network recognition skills. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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Article
Architecture-Oriented Agent-Based Simulations and Machine Learning Solution: The Case of Tsunami Emergency Analysis for Local Decision Makers
Information 2023, 14(3), 172; https://doi.org/10.3390/info14030172 - 08 Mar 2023
Viewed by 1188
Abstract
Tsunamis are a perilous natural phenomenon endangering growing coastal populations and tourists in many seaside resorts. Failures in responding to recent tsunami events stresses the importance of further research in building a robust tsunami warning system, especially in the “last mile” component. The [...] Read more.
Tsunamis are a perilous natural phenomenon endangering growing coastal populations and tourists in many seaside resorts. Failures in responding to recent tsunami events stresses the importance of further research in building a robust tsunami warning system, especially in the “last mile” component. The lack of detail, unification and standardisation in information processing and decision support hampers wider implementation of reusable information technology solutions among local authorities and officials. In this paper, the architecture of a tsunami emergency solution is introduced. The aim of the research is to present a tsunami emergency solution for local authorities and officials responsible for preparing tsunami response and evacuation plans. The solution is based on a combination of machine learning techniques and agent-based modelling, enabling analysis of both real and simulated datasets. The solution is designed and developed based on the principles of enterprise architecture development. The data exploration follows the practices for data mining and big data analyses. The architecture of the solution is depicted using the standardised notation and includes components that can be exploited by responsible local authorities to test various tsunami impact scenarios and prepare plans for appropriate response measures. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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Article
A Roadside and Cloud-Based Vehicular Communications Framework for the Provision of C-ITS Services
Information 2023, 14(3), 153; https://doi.org/10.3390/info14030153 - 01 Mar 2023
Cited by 2 | Viewed by 1458
Abstract
Road infrastructure plays a critical role in the support and development of the Cooperative Intelligent Transport Systems (C-ITS) paradigm. Roadside Units (RSUs), equipped with vehicular communication capabilities, traffic radars, cameras, and other sensors, can provide a multitude of vehicular services and enhance the [...] Read more.
Road infrastructure plays a critical role in the support and development of the Cooperative Intelligent Transport Systems (C-ITS) paradigm. Roadside Units (RSUs), equipped with vehicular communication capabilities, traffic radars, cameras, and other sensors, can provide a multitude of vehicular services and enhance the cooperative perception of vehicles on the road, leading to increased road safety and traffic efficiency. Moreover, the central C-ITS system responsible for overseeing the road traffic and infrastructure, such as the RSUs, needs an efficient way of collecting and disseminating important information to road users. Warnings of accidents or other dangers, and other types of vehicular services such as Electronic Toll Collection (ETC), are examples of the types of information that the central C-ITS system is responsible for disseminating. To remedy these issues, we present the design of an implemented roadside and cloud architecture for the support of C-ITS services. With the main objectives of managing Vehicle-to-Everything (V2X) communication units and network messages of a public authority or motorway operator acting as a central C-ITS system, the proposed architecture was developed for different mobility testbeds in Portugal, under the scope of the STEROID research project and the pan-European Connected Roads (C-Roads) initiative. RSUs, equipped with ETSI ITS-G5 communications, are deployed with a cellular link or fiber optics connection for remote control and configuration. These are connected to a cloud Message Queuing Telemetry Transport (MQTT) broker where communication is based on a geographical tiling scheme, which allows the selection of the appropriate coverage areas for the dissemination of C-ITS messages. The architecture is deployed in the field, on several Portuguese motorways, where road traffic and infrastructure are monitored through a C-ITS platform with visualization and event reporting capabilities. The provided architecture is independent of the underlying communication technology and can be easily adapted in the future to support Cellular-V2X (PC5 interface) or 5G RSUs. Performance results of the deployed architecture are provided. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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Article
Multi-Dimensional Information Alignment in Different Modalities for Generalized Zero-Shot and Few-Shot Learning
Information 2023, 14(3), 148; https://doi.org/10.3390/info14030148 - 24 Feb 2023
Cited by 1 | Viewed by 1022
Abstract
Generalized zero-shot learning (GZSL) aims to solve the category recognition tasks for unseen categories under the setting that training samples only contain seen classes while unseen classes are not available. This research is vital as there are always existing new categories and large [...] Read more.
Generalized zero-shot learning (GZSL) aims to solve the category recognition tasks for unseen categories under the setting that training samples only contain seen classes while unseen classes are not available. This research is vital as there are always existing new categories and large amounts of unlabeled data in realistic scenarios. Previous work for GZSL usually maps the visual information of the visible classes and the semantic description of the invisible classes into the identical embedding space to bridge the gap between the disjointed visible and invisible classes, while ignoring the intrinsic features of visual images, which are sufficiently discriminative to classify themselves. To better use discriminative information from visual classes for GZSL, we propose the n-CADA-VAE. In our approach, we map the visual feature of seen classes to a high-dimensional distribution while mapping the semantic description of unseen classes to a low-dimensional distribution under the same latent embedding space, thus projecting information of different modalities to corresponding space positions more accurately. We conducted extensive experiments on four benchmark datasets (CUB, SUN, AWA1, and AWA2). The results show our model’s superior performance in generalized zero-shot as well as few-shot learning. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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Article
Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems
Information 2023, 14(2), 131; https://doi.org/10.3390/info14020131 - 17 Feb 2023
Viewed by 1691
Abstract
In today’s technology-driven society, many decisions are made based on the results provided by machine learning algorithms. It is widely known that the models generated by such algorithms may present biases that lead to unfair decisions for some segments of the population, such [...] Read more.
In today’s technology-driven society, many decisions are made based on the results provided by machine learning algorithms. It is widely known that the models generated by such algorithms may present biases that lead to unfair decisions for some segments of the population, such as minority or marginalized groups. Hence, there is concern about the detection and mitigation of these biases, which may increase the discriminatory treatments of some demographic groups. Recommender systems, used today by millions of users, are not exempt from this drawback. The influence of these systems on so many user decisions, which in turn are taken as the basis for future recommendations, contributes to exacerbating this problem. Furthermore, there is evidence that some of the most recent and successful recommendation methods, such as those based on graphical neural networks (GNNs), are more sensitive to bias. The evaluation approaches of some of these biases, as those involving protected demographic groups, may not be suitable for recommender systems since their results are the preferences of the users and these do not necessarily have to be the same for the different groups. Other assessment metrics are aimed at evaluating biases that have no impact on the user. In this work, the suitability of different user-centered bias metrics in the context of GNN-based recommender systems are analyzed, as well as the response of recommendation methods with respect to the different types of biases to which these measures are addressed. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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Article
A Thorough Reproducibility Study on Sentiment Classification: Methodology, Experimental Setting, Results
Information 2023, 14(2), 76; https://doi.org/10.3390/info14020076 - 28 Jan 2023
Viewed by 1068
Abstract
A survey published by Nature in 2016 revealed that more than 70% of researchers failed in their attempt to reproduce another researcher’s experiments, and over 50% failed to reproduce one of their own experiments; a state of affairs that has been termed the [...] Read more.
A survey published by Nature in 2016 revealed that more than 70% of researchers failed in their attempt to reproduce another researcher’s experiments, and over 50% failed to reproduce one of their own experiments; a state of affairs that has been termed the ‘reproducibility crisis’ in science. The purpose of this work is to contribute to the field by presenting a reproducibility study of a Natural Language Processing paper about “Language Representation Models for Fine-Grained Sentiment Classification”. A thorough analysis of the methodology, experimental setting, and experimental results are presented, leading to a discussion of the issues and the necessary steps involved in this kind of study. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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Article
NeuralMinimizer: A Novel Method for Global Optimization
Information 2023, 14(2), 66; https://doi.org/10.3390/info14020066 - 25 Jan 2023
Cited by 1 | Viewed by 1115
Abstract
The problem of finding the global minimum of multidimensional functions is often applied to a wide range of problems. An innovative method of finding the global minimum of multidimensional functions is presented here. This method first generates an approximation of the objective function [...] Read more.
The problem of finding the global minimum of multidimensional functions is often applied to a wide range of problems. An innovative method of finding the global minimum of multidimensional functions is presented here. This method first generates an approximation of the objective function using only a few real samples from it. These samples construct the approach using a machine learning model. Next, the required sampling is performed by the approximation function. Furthermore, the approach is improved on each sample by using found local minima as samples for the training set of the machine learning model. In addition, as a termination criterion, the proposed technique uses a widely used criterion from the relevant literature which in fact evaluates it after each execution of the local minimization. The proposed technique was applied to a number of well-known problems from the relevant literature, and the comparative results with respect to modern global minimization techniques are shown to be extremely promising. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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Article
Improving the Adversarial Robustness of Neural ODE Image Classifiers by Tuning the Tolerance Parameter
Information 2022, 13(12), 555; https://doi.org/10.3390/info13120555 - 26 Nov 2022
Viewed by 1117
Abstract
The adoption of deep learning-based solutions practically pervades all the diverse areas of our everyday life, showing improved performances with respect to other classical systems. Since many applications deal with sensible data and procedures, a strong demand to know the actual reliability of [...] Read more.
The adoption of deep learning-based solutions practically pervades all the diverse areas of our everyday life, showing improved performances with respect to other classical systems. Since many applications deal with sensible data and procedures, a strong demand to know the actual reliability of such technologies is always present. This work analyzes the robustness characteristics of a specific kind of deep neural network, the neural ordinary differential equations (N-ODE) network. They seem very interesting for their effectiveness and a peculiar property based on a test-time tunable parameter that permits obtaining a trade-off between accuracy and efficiency. In addition, adjusting such a tolerance parameter grants robustness against adversarial attacks. Notably, it is worth highlighting how decoupling the values of such a tolerance between training and test time can strongly reduce the attack success rate. On this basis, we show how such tolerance can be adopted, during the prediction phase, to improve the robustness of N-ODE to adversarial attacks. In particular, we demonstrate how we can exploit this property to construct an effective detection strategy and increase the chances of identifying adversarial examples in a non-zero knowledge attack scenario. Our experimental evaluation involved two standard image classification benchmarks. This showed that the proposed detection technique provides high rejection of adversarial examples while maintaining most of the pristine samples. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2023)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Bias assessment approach for addressing recommendation fairness in GNN-based recommender systems
Authors: Nikzad Chizari, Keyvan Tajfar, Niloufar Shoeibi, and María N. Moreno-García
Affiliation: --
Abstract: In today's technology-driven society, many decisions are made based on the results provided by machine learning algorithms. It is widely known that the models generated by such algorithms may present biases that lead to unfair decisions for some segments of the population, such as minority or marginalized groups. Hence the great current concern for the detection and mitigation of these biases that may increase the discriminatory treatment of some demographic groups. Recommender systems, used today by millions of users, are not exempt from this drawback. Therefore, their influence on so many user decisions, which in turn are taken as the basis for future recommendations, only exacerbates this problem. Furthermore, there is evidence that some of the more recent and successful recommendation methods, such as those based on Graphical Neural Networks (GNN), are more sensitive to bias. The evaluation approaches of some of these biases, such as those involving protected demographic groups, may not be suitable for recommender systems since their results are the preferences of the users and these do not necessarily have to be the same for the different groups. In this work, the suitability of different bias metrics in the context of GNN-based recommender systems are analysed, and a new strategy specifically targeted to this domain is presented.

Title: Performance comparison among PSO, DE and Grey Wolf for optimizing chaotic systems
Authors: Maria Fernanda Moreno-Lopez, Angel Joel Lara-Martinez, Astrid Maritza Gonzalez-Zapata, Alejandro Silva-Juarez, Luis Gerardo de la Fraga, Esteban Tlelo-Cuautle
Affiliation: --
Abstract: The optimization of chaotic systems remains a challenge because the search space of the parameters can have several orders of magnitude so that both the equilibrium points and corresponding eigenvalues can be very sparse. This imposes the need of estimating the step-size $h$ of the numerical method, as for example: a 3D system has three eigenvalues so that $h$ is estimated taking into account the lowest eigenvalue and the total time simulation taking the highest one. In this manner, a chaotic system is optimized herein applying the single-objective optimization algorithms known as: particle swarm optimization (PSO), differential evolution (DE) and grey wolf (GW). In these algorithms, the constraints are that a chaotic system must have complex eigenvalues and the Fourier transform of the time series must have a certain threshold. The objective function is associated to the Kaplan-Yorke dimension and then PSO, DE and GW are run to generate feasible solutions that are validated using TISEAN. The best 5 solutions of each algorithm are listed and compared with respect to the Kaplan-Yorke dimension.

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