Reviewerly/exHarmony huggingface dataset:
Research on the Application of Metaverse Technology in the Field of Intelligent Transportation

The combination of metaverse and transportation provides new infinite possibilities for the future development of intelligent transportation. The metaverse-empowered intelligent transportation has the characteristics from single to multiple dimensions, from reality to virtual-real co-prosperity, from high-carbon to low-carbon, and has been widely used in disaster accident simulation, car driving skills training, traffic and travel game experience, Maas and other fields. At the same time, the metaverse faces challenges in the application of intelligent transportation, such as incomplete policies and regulations, threats to information security, and monopolies in data sharing. While making good use of the metaverse to bring development opportunities to intelligent transportation, it is also necessary to further improve policy supervision systems, data sharing systems, and information protection mechanisms to prevent potential impacts and risks.
Contextual Challenges to Explainable Driving Automation: The Case of Machine Perception

As happens in the case of many Artificial Intelligence systems, explainability has been acknowledged as a relevant ethical value also for driving automation. This chapter discusses the challenges raised by the application of explainability to driving automation. Indeed, designing explainable automated vehicles is not a straightforward task. On the one hand, technical constraints must be considered to integrate explainability without impairing the overall performance of the vehicle. On the other hand, explainability requirements vary depending on the human stakeholders and the technological functions involved, thus further complicating its embedment. The goal of the chapter is thus to investigate what explainability means with reference to driving automation. To this aim, we focus on machine perception and explore the related explainability requirements and challenges. We argue that explainability is a multifaceted concept that needs to be differently articulated in different contexts. Paying due attention to the contextual aspects of explainability, in particular the content of the explanations and the stakeholders they are addressed to, is critical to serve the ethical values it is supposed to support.
Postdigital Nature(s)

Nature is not only the fabric and crucible out of which all human life comes into being, it is also the source of an infinite variety of inspirational images, ideas, and narratives for people to think and feel with, both as individuals and as sociocultural groups. Nature is a very large concept that is difficult to succinctly yet adequately define, but can be collapsed into three large, intersecting domains, covering: the discursive realm of ideas and symbolism; the empirical realm in which humans interact with/in the natural world; and the theoretical world of causality—the laws of nature. Postdigital nature adds a fourth dimension: nature as a resource for the generation of digital data. This entry considers the status of nature in the postdigital era, and links between postdigital nature and related terms, including virtual nature, network nature, and post-nature.
Adversarial Attacks Assessment of Salient Object Detection via Symbolic Learning

Machine learning is at the center of mainstream technology and outperforms classical approaches to handcrafted feature design. Aside from its learning process for artificial feature extraction, it has an end-to-end paradigm from input to output, reaching outstandingly accurate results. However, security concerns about its robustness to malicious and imperceptible perturbations have drawn attention since its prediction can be changed entirely. Salient object detection is a research area where deep convolutional neural networks have proven effective but whose trustworthiness represents a significant issue requiring analysis and solutions to hackers’ attacks. Brain programming is a kind of symbolic learning in the vein of good old-fashioned artificial intelligence. This work provides evidence that symbolic learning robustness is crucial in designing reliable visual attention systems since it can withstand even the most intense perturbations. We test this evolutionary computation methodology against several adversarial attacks and noise perturbations using standard databases and a real-world problem of a shorebird called the Snowy Plover portraying a visual attention task. We compare our methodology with five different deep learning approaches, proving that they do not match the symbolic paradigm regarding robustness. All neural networks suffer significant performance losses, while brain programming stands its ground and remains unaffected. Also, by studying the Snowy Plover, we remark on the importance of security in surveillance activities regarding wildlife protection and conservation.
Postdigital Time

The ‘postdigital’ is an inherently temporal concept yet, along with the postdigital itself, the temporality of the postdigital remains open. With few exceptions, by rejecting the idea that the ‘postdigital’ comes right after the ‘digital’ in a successive manner, most scholars interpret the temporality of the concept in a way that is admittedly blurry and indeterminate. This opens various lines of inquiry. How should we understand and relate to postdigital time, including the role digital technologies play in it? What are the constraints it imposes on our imagination of the future and present? How does it naturalize the contemporary order?
Semantics-Preserving Sketch Embedding for Face Generation

With recent advances in image-to-image translation tasks, remarkable progress has been witnessed in generating face images from sketches. However, existing methods frequently fail to generate images with details that are semantically and geometrically consistent with the input sketch, especially when various decoration strokes are drawn. To address this issue, we introduce a novel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {W}$</tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {W^+}$</tex-math></inline-formula> encoder architecture to take advantage of the high expressive power of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {W^+}$</tex-math></inline-formula> space and semantic controllability of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {W}$</tex-math></inline-formula> space. We introduce an explicit intermediate representation for sketch semantic embedding. With a semantic feature matching loss for effective semantic supervision, our sketch embedding precisely conveys the semantics in the input sketches to the synthesized images. Moreover, a novel sketch semantic interpretation approach is designed to automatically extract semantics from vectorized sketches. We conduct extensive experiments on both synthesized sketches and hand-drawn sketches, and the results demonstrate the superiority of our method over existing approaches on both semantics-preserving and generalization ability.
An Alternative Graphical Modeling for Management Visualization Analysis (MVA) Under the Uses of Econographicology

The rationale of Econographicology revolves around the efficacy of multidimensional graphs as the most effective visual tool to understand any Management phenomenon from a multidimensional view. The main motivation behind the creation of Econographicology is to evaluate multidimensional graphs evolved so far in Management Visualization Analysis (MVA) and to develop new type of multidimensional graphs to facilitate the study of MVA as well as Management and economics. Thereby, the mission of Econographicology is to offer academics, researchers and policy maker’s an alternative multidimensional graphical modeling approach for the research and teaching-learning process of management, business, and economics from a multidimensional perspective. Hence, this alternative multidimensional graphical modeling approach is offering a set of multi-dimensional coordinate spaces to build different types of multidimensional graphs to study any Management phenomenon. The following new types of multi-dimensional coordinate spaces are presented: the 5-dimensional coordinate space (vertical position and horizontal position), the mega-dynamic disks coordinate space (vertical position and horizontal position), and the mega-disks networks mapping (MDN-Mapping).
An Alternative Graphical Modeling for Business Visualization Analysis (FVA) Under the Uses of Econographicology

The rationale of Econographicology revolves around the efficacy of multidimensional graphs as the most effective visual tool to understand any business phenomenon from a multidimensional view. The main motivation behind the creation of Econographicology is to evaluate multidimensional graphs evolved so far in Business Visualization Analysis (BVA) and to develop new type of multidimensional graphs to facilitate the study of BVA as well as finance and economics. Thereby, the mission of Econographicology is to offer academics, researchers and policy maker’s an alternative multidimensional graphical modeling approach for the research and teaching-learning process of business, finance, and economics from a multidimensional perspective. Hence, this alternative multidimensional graphical modeling approach is offering a set of multi-dimensional coordinate spaces to build different types of multidimensional graphs to study any business phenomenon. The following new types of multi-dimensional coordinate spaces are presented: the 5-dimensional coordinate space (vertical position and horizontal position), the mega-dynamic disks coordinate space (vertical position and horizontal position), and the mega-disks networks mapping (MDN-Mapping).
NAÏVE BAYES ALGORITHM OPTIMIZATION USING PARTICLE SWARM OPTIMIZATION (PSO) FOR COVID-19 VACCINE SENTIMENT ANALYSIS ON TWITTER

The Covid-19 vaccine is a vaccine that is quite popular, because it is the most needed and most discussed vaccine. There are 5 types of vaccines that are very popular including AstraZeneca, Moderna, Pfizer, Sinopharm and Sinovac. Sentiment analysis is a branch of text classification with computational linguistics and natural language processing that refers to a broad field, and text mining has a function to analyze opinions, judgments, sentiments, attitudes, evaluations and emotions of a person regarding an individual, organization, certain topics, services and other activities. This study aims to classify public sentiment towards the type of Covid-19 vaccine on social media Twitter, whether the opinion is positive or negative by using the Naïve Bayes algorithm based on Particle Swarm Optimization (PSO). The conclusion of this study is that the results of testing the Naïve Bayes algorithm with PSO using RapidMiner software are 79.17% accuracy, 87.69% precision, 85.07% recall for AstraZeneca vaccine, 68.82% accuracy, 92.29% precision, 71.72% recall for Moderna vaccine, 67.54% accuracy, precision 77.83%, recall 62.95% for Pfizer vaccine, accuracy 93.33%, precision 91.67%, recall 100.00% for Sinopharm vaccine, and accuracy 74.93%, precision 82.61%, recall 70.90% for Sinovac vaccine. It can be concluded that with the help of optimization PSO, the resulting confusion matrix value is greater and is proven to be more accurate. Keywords : Vaccine; Covid-19; Sentiment Analysis; Naive Bayes; Particle Swarm Optimization.
Dimension Hopper: A Tool for Exploring Multidimensional Data in Design Research

Design research methods frequently involve the collection of field data – such as sensor data or subjective experience - to understand human activities and practices. However, exploring multidimensional data is challenging for designers, as it requires switching between different data types and relating them to identify larger patterns and understand the context behind observations. Our study addresses this problem by presenting Dimension Hopper - an interactive data visualization tool that uses Multiple Coordinated Views (MCV) to facilitate insight discovery. We demonstrate how the tool can aid in data exploration by presenting examples of how it can be used to identify behavioral patterns in field data. We also discuss the challenges that emerged in our initial exploration of the tool, leading to factors that need to be considered when designing visualization tools for exploring multidimensional field data in design research.
Methodological framework and tools for the participatory design of spatial interactive installations of augmented and mixed reality in arts and culture.

Through different projects related to spatial augmented reality, a large gap was observed in the methodology of designing and evaluating such systems due to the large differentiation between them. Aiming at the efficient design of such systems, the researcher proposes this particular PhD dissertation to facilitate both colleagues in the same field and collaborators from other fields that are working together to create an exhibit or system. Although the thesis is still in its infancy, important questions have been recorded for later study.
Methodologies and Ethical Considerations in Phishing Research: A Comprehensive Review

Phishing is a significant security threat that causes financial and reputational losses to end-users and service providers in modern information systems. Current anti-phishing research is fragmented and does not address the issue from a pervasive computing perspective. As phishing attacks exploit human susceptibility, designing appropriate and personalized anti-phishing security frameworks that consider individual behavior is crucial. Phishing experiments raise ethical and legal concerns. Researchers carry out experiments to measure the probability and frequency that something vulnerable could happen, using this information to identify the most effective protection measures. This paper aims to identify ethical and practical issues related to the creation and execution of phishing experiments. The review examines the types of experiments conducted, ethical rules applied, user consent obtained, and the types of phishing examined in the experiments. Our aim in this review is to focus on identifying legal white spaces and ethical considerations by providing a complete review of the current approaches and processes used in phishing experiments.
Usability testing and redesign of a mobile application for real estate

The real estate industry is witnessing a growing trend of using mobile applications for property searches, fueled by the rising popularity of mobile technology. However, despite the widespread adoption of these apps on a global scale, their usage, especially within the Greek market, has not been adequately evaluated, particularly concerning their usability and user experience aspects. The study investigates the usability assessment of "Spitogatos", a popular real estate application in Greece. A representative group of 10 users was involved in the research, to assess the convenience and efficiency of the property search and securing process within the application. The testing phase revealed several usability issues, primarily related to features such as location search, editing search results, saving and removing properties, and applying filters. The analysis of user feedback and observations also revealed challenges with certain screens lacking a clear visual hierarchy, causing confusion and frustration among users. Based on the findings, several recommendations are proposed to enhance the application's usability, including a user interface revamp of eleven screens. Finally, the study discusses the importance of incorporating iterative usability testing into the design process, future research directions and acknowledges its limitations.
Exploring the usability of mobile augmented reality interactions in relation with primary school students' level of cognitive abilities.

Augmented Reality (AR) is an emerging technology with measurable benefits for students and teachers. However, it is sometimes difficult to use, especially for young-aged users, which may negatively affect learning experience. Many researchers agree that to achieve the technology’s unique benefits, AR designs should take into consideration students’ abilities and limitations that derive from their physical and cognitive development. In the case of primary school students (aged 6-12), the need for appropriately designed AR experiences is higher, as children’s abilities are still developing at this period of their lives. The present research aims to understand how children’s level of motor and spatial abilities relate to the use of certain interaction types of mobile AR. Students’ cognitive scores will be correlated with usability metrics and performance data, while using four different interaction types in a specially designed mobile AR game application. The results may lead to the formation of a set of guidelines for designing usable mobile AR applications that comply with primary students’ cognitive development.
Estimating the AUC of mixture MROC curve in the presence of measurement errors

In a classification scenario, we usually come across data with and without class labels. If the class labels of individuals are unknown or masked by hidden components, the classifier rules must include the identification of the actual number of subcomponents in the data. Also, the presence of measurement errors in the data may influence the measures of the receiver operating characteristic model. In this paper, a mixture of multivariate receiver operating characteristic models is proposed to deal with multi-model patterns in the data, and a bias-corrected estimator is derived for estimating the area under the curve of the proposed model. The proposed methodology is supported by the real dataset and simulation studies.
Offline writer identification using deep feature concatenation

Handwriting is an individual trait that serves as evidence to authenticate a particular writer. Identifying the writer of a handwritten text has shown encouraging results in examining historical and forensic documents. In this paper, we propose a novel offline writer identification system based on the challenging analysis of small amount of data to extract distinct patterns. In our deep network, the feature extraction process relies on a specially designed dual-path architecture, and the resulting embeddings are concatenated to produce the final learned features. To deal with a variety of uncertainties such as high intra-class variations and noises, we leverage the fuzzy logic in the design of a custom Convolutional Neural Network (CNN) with a type-2 fuzzy activation function for the first path. Additionally, the second path utilizes the transfer learning-based CNN to enhance the discriminability of the learned features. Our method allows for text-independent writer identification, eliminating the need for identical handwriting samples to train and test the model. Considering that various factors can influence the handwriting style, a dataset containing right-to-left handwriting samples is assembled. The proposed method is evaluated on our developed dataset and four widely-known public datasets, namely KHATT, CVL, Firemaker, and IAM. High accuracy values are achieved, with results of 99.85%, 99.83%, 99.79%, 99.64%, and 98.17% for each dataset, respectively. One noteworthy aspect of this study is that the evaluation results on diverse datasets demonstrate the applicability of the proposed model to various languages. Moreover, the model performs effectively in real-world scenarios with limited handwritten data.
Species' Citadel's Timeline: A Virtual Reality Experience in a peripheral Archaeological Museum

This study explores the effectiveness of VR escape games as engaging learning tools for archaeological venues. University students participated in the study to assess their motivation and immersion while using a VR escape room game set in a real archaeological venue. The game replicated the museum building in a virtual environment and incorporated scanned artifacts as interactive objects. Participants solved riddles related to the artifacts, creating an immersive and interactive storytelling experience. The study’s results were promising, showing that the VR escape room game successfully motivated participants. The use of scanned artifacts and the realistic venue enhanced immersion. The findings highlight the potential of VR escape games for engaging students with archaeological content and fostering motivation and understanding of artifacts.
Co-clustering contaminated data: a robust model-based approach

Abstract The exploration and analysis of large high-dimensional data sets calls for well-thought techniques to extract the salient information from the data, such as co-clustering. Latent block models cast co-clustering in a probabilistic framework that extends finite mixture models to the two-way setting. Real-world data sets often contain anomalies which could be of interest per se and may make the results provided by standard, non-robust procedures unreliable. Also estimation of latent block models can be heavily affected by contaminated data. We propose an algorithm to compute robust estimates for latent block models. Experiments on both simulated and real data show that our method is able to resist high levels of contamination and can provide additional insight into the data by highlighting possible anomalies.
Measuring the Desirability of an Intelligent Advisor for Predicting the Perceived Quality of News Articles

Abstract. This paper presents the initial findings of the iQJournalism system’s prototype, an effort to create an intelligent advisor for predicting the perceived quality of news articles. Accordingly, artificial intelligence methodologies are utilized, with the purpose of providing real-time recommendations to journalists looking to improve the overall quality and engagement of their articles. The iQJournalism is designed using a user-centered design approach, in order to facilitate the specific needs of the journalists and editors using it. In this paper we discuss preliminary results of a study that was conducted with 20 users. Following an extensive literature review and the insights of a focus group with 10 professional journalists and MSc students, we created an interactive prototype of the iQJournalism system. The main aim is to facilitate the interaction of the users with the computational layer of the system for estimating the perceived quality of an article before its release. We organized a moderated desirability (light usability) study, in order to capture the users’ feedback. We used user experience and usability measurement tools like UEQ, SUS, Product reaction cards, perceived satisfaction and system adoption items, and open-ended questions to collect more comprehensive insights around the acceptance and usefulness of our prototype. Initial results show an overall favorable user experience, effective and efficient interaction when users engage with a series of situation-specific tasks.