Reviewerly/exHarmony huggingface dataset:
Overview and Tendencies of Augmented Reality Applications in Medicine

Recently, augmented reality (AR) has received increasing attention, with the development of AR devices such as head-mounted displays, haptic devices, and AR glasses. Healthcare can be considered to be one of the most effective applications of AR. In this article, we describe a literature systematic mapping conducted to investigate the state-of-the-art AR technology in order to help doctors and future doctors in a wide range of applications and opportunities. The 160 studies that were ultimately collected were categorized into 3 representative topics: main goals of using AR in healthcare, the most used technologies, and how these studies were evaluated or measured. Finally, we reflected on the biggest challenges for future research and opportunities in using AR next years. At this point, improvements in tracking techniques and appropriate assessments of the tool in a clinician’s daily routine could be essential for identifying points that were not seen in controlled testing scenarios.
A CASE Tool for the Socially-Aware Persuasive System Design toward enhancing the Human Behavior

The design of persuasive information systems is taking importance especially those focused on sustainability. The participation of stakeholders in the process of designing persuasive information systems is hardly supported by tools available online. In this work we present a CASE Tool to aid the design of persuasive information systems. The Tool was designed, developed, evaluated with a case study and integrated to the OpenDesign platform to be available online to a wide audience. This CASE Tool is composed by artifacts of a process as a support to the first steps at the systems design phase. Our foundations take into account the socially-aware persuasive system design framework, with its persuasive principles, artefacts and process. Persuading and influencing people to achieve a target behavior through technologies can be more facilitated if designers have a CASE Tool as proposed.
Personal Data Comics: A Data Storytelling Approach Supporting Personal Data Literacy

Most people interact with digital technologies that collect personal data about their behavior and experiences, leaving behind a data trail. The data within this trail is abstract and difficult to interpret; still, people often need to decide about its collection and distribution. Hence, it is paramount to support personal data literacy, for which data visualization approaches have been successful. These approaches focus mostly on data from single sources (e.g., IoT devices at home) or types (e.g., menstrual logs) and fail to capture people’s situated knowledge. We hypothesize that creating data comics can address these limitations and support people in developing personal data literacy. In this paper, we explore how non-data experts create personal data comics, starting from simple data visualizations, and investigate their effectiveness and engagement in the context of pregnancy. Doing so, we identify comic elements that facilitate the autonomous exploration of personal data and provide design recommendations to support independent data comic creation.
GenQ: Automated Question Generation to Support Caregivers While Reading Stories with Children

When caregivers ask open-ended questions to motivate dialogue with children, it facilitates the child's reading comprehension skills. Although there is scope for use of technological tools, referred here as “intelligent tutoring systems”, to scaffold this process, it is currently unclear whether existing intelligent systems that generate human-language like questions is beneficial. Additionally, training data used in the development of these automated question generation systems is typically sourced without attention to demographics, but people with different cultural backgrounds may ask different questions. As a part of a broader project to design an intelligent reading support app for Latinx children, we crowd–sourced questions from Latinx caregivers and non-caregivers as well as caregivers and non-caregivers from other demographics. We examine variations in question-asking within this dataset mediated by individual, cultural, and contextual factors. We then design a system that automatically extracts templates from this data to generate open-ended questions that are representative of those asked by Latinx caregivers.
Data Storytelling: Revolutionising Human-Data Interaction or Just Passing Hype?

Supporting stakeholders in various sectors to interpret dashboards and visualisations presents significant design challenges that might often be overlooked. The interpretation of visualised data by decision-makers and professionals is essentially the construction of a narrative about the underlying processes. Implementing data storytelling techniques in the design of these visualisations can foster deeper insights by aligning the intended objectives, goals, and outcomes with visual elements. The purpose of this tutorial is to guide participants in integrating data storytelling techniques into their visualisation and dashboard designs, ensuring the communication of meaningful insights.
Planning and Conducting Heuristic Evaluations with DUXAIT-NG

This workshop will offer participants the opportunity to engage in a face-to-face learning experience. The main objective of this workshop is to equip participants with the essential skills and knowledge necessary to plan and conduct heuristic evaluations using the DUXAIT-NG platform.
A Serious Game prototype aimed at rescuing the Ixcatecan language using the Design Thinking methodology

Santa María Ixcatlán is a community located in the Cañada Oaxacan Region, currently there are only four speakers of the Ixcatecan language, for this reason it is important to rescue this indigenous language. Following the phases of the Design Thinking methodology, a contextual study was carried out with interviews and empathy maps to detect the needs of the users, all the possible solutions were analyzed and the best one was chosen, then a serious game prototype was designed for the learning of the Ixcateco, this includes vocabulary of animals, colors and fruits, finally, the design was evaluated through usability tests with five users, the results obtained were 100% efficacy, 100% efficiency and 8.45 satisfaction rating.
DoTA: DesignOps Teams Assessment of agile software development teams in a financial institution

The emergence of interest in design operations, or simply DesignOps, has been growing in the software industry and there is a need for research in order to assist in understanding it. In this context, the objective of this work was to build a model to evaluate design operations in teams that use agile methods. This is applied research and, regarding the approach, it is a combined or mixed research, based on the evaluation design model. The methodology used to build DoTA - DesignOps Teams Assessment - was to carry out an integrative literature review to map the current state-of-the art in design operations research, data collection through exploratory survey, construction of the assessment instrument employed in the maturity assessment of five DesignOps teams in the development of mobile applications for a financial institution and subsequent diagnosis of the instrument by the participants of the evaluation process. It is structured as an assessment involving three evaluation dimensions (people, processes, and ecosystem), subdivided into ten skills (organize, collaborate, grow, standardize, share, prioritize, build, measure, socialize, disseminate). This arrangement that involves dimensions and skills was the basis for its construction, presenting clear and measurable aspects to evaluate design operations. Most of the assessed teams showed an intermediate level of maturity in DesignOps, with teams being appointed as practitioners, characterized by the understanding of the function and value of DesignOps by the leader or some members of the team. The Cambridge Process Approach was used to evaluate DoTA. The results support the validity of DoTA, in the financial institution where it was applied, regarding its feasibility, utility and usability.
Applying Heuristic Evaluation with Different Evaluator Profiles: A Comparative Study Between Novice and Expert Specialists

Currently, usability is an extremely relevant aspect that must be considered during the construction process of a software product. The importance of this quality attribute for both developers and companies has led professionals to establish methods to determine if interaction interfaces are usable, understandable, and attractive. Heuristic evaluation is one of the methods that stands out for its efficiency and simplicity. However, the authors establish different ways of executing and applying this assessment method. Some authors establish that only specialists in the area of HCI (Human-Computer Interaction) should participate, while others mention that it is possible the method might be conducted by specialists from related fields. In this study, we show the differences in the results when the heuristic evaluation is employed by experts, in contrast when the method is used by people with basic knowledge (novices). Likewise, the study shows the effectiveness of a web application to support the inspection process built with a basis on an exhaustive analysis of the literature. The conclusions of this research state that there are no significant differences and that the heuristics can be applied in both scenarios with high reliability.
Learning Interaction User Flow

This study centers on the development of augmented reality (AR) learning scenarios through the integration of the 4C/ID instructional model with the User-flow graphical notation. Its primary objective is to offer non-technical educators and designers a straightforward and efficient tool for specifying user tasks within learning environments. The Learning Interaction User-flow (LUI) notation has been specifically designed for this purpose, allowing for a visual depiction of user tasks without the intricacies and challenges associated with other technical notations. Within this research, a graphical notation is presented alongside a study case and a pilot assessment involving expert users in graphical user interface (GUI) design. The results obtained underscore the potential and ease of use of this notation in representing learning tasks within augmented reality scenarios.
Beyond the Physical and Virtual for a Socioenactive Interaction

The rapid development of technology, widespread in our daily lives, is allowing the design of more complex ubiquitous environments. These challenges can lead to design opportunities and bring to the Human-Computer Interaction (HCI) community an opportunity to revisit Weiser’s original ideas about ubiquitous computing and new perspectives on interaction. According to Weiser’s vision, computing should not take us away from the world around us, and from other people. Therefore, this paper aims to uncover aspects related to intersubjectivity presented in a remote experience of children’s interaction with the Aquarela Virtual System (AVS). The AVS allows remotely situated children to interact with other children by using objects enriched with technology, which can be their own toys, related to elements of the Aquarela song. Also, children can express their emotional states with emojis. The AVS was designed from a socioenactive perspective on interaction that emphasizes the mutual influence between human actions, the environment formed by ubiquitous technologies, and our interpersonal relationships with others. We applied a Thematic Analysis approach to data collected from interaction of a group of children in order to find common patterns. Findings of the analysis allow us to raise different aspects of intersubjectivity, contributing to inform the design of ubiquitous systems in a socioenactive perspective.
Redefining e-commerce engagement: exploring the demographic impact of WhatsApp chatbots in the Mexican market

This study examines the effectiveness of a WhatsApp e-commerce chatbot, integrated with agent support, implemented for The Coca-Cola Company (TCCC) in the Mexican market. The engineering process, chatbot design, development, and the expansion of this concept to a global chatbot orchestrator are discussed. Using Google Analytics, data from 2022 is analyzed, focusing on specific demographic segments and comparing the chatbot’s performance with its web-based counterpart. Findings reveal a counterintuitive result: the WhatsApp e-commerce chatbot appeals to adults-older adults (+45 years) and females consumers for FMCG, Food and Beverages category, leading to higher conversion rates than the conventional web channel for this groups. Results suggest that managing a botview and web channel independently allows for tailored optimization strategies for each demographic segment. Chatbot surpassed its web-based counterpart in e-commerce total revenue contribution in 2022, in the market with the highest penetration of the Coca-Cola brand globally, becoming the primary reference channel for consumers. This study proposes that WhatsApp chatbots have the potential to become an established omnichannel reference for e-commerce.
Organization of project-based training of students in terms of social partnership between a college, university and enterprise

The subject of the study is the process of organizing and implementing project-based training for university and college students in a social partnership created in the system of educational and professional interaction between college-university-enterprise. The object of the research is the organization and technology of project-based training of students in the development of technological processes for manufacturing parts, implemented in the system of educational and professional interaction college-university-enterprise. The authors consider in detail such aspects of the topic as options for organizing social partnership of educational organizations of secondary vocational, higher education and industrial enterprises through the use of real projects and simulation of professional activity of a specialist in the project activities of students. The authors consider in detail the step-by-step methodology for the implementation of project-based learning technology in the course of technological training of students, focused on the formation of professional competencies and labor functions of professional standards. The results of a pedagogical experiment on the formation of technological competencies during the implementation of project-based learning in a professionally oriented educational space are presented. Main research methods: theoretical analysis of the experience of organizing project-based learning, synthesis of didactic tools and methods of conducting training sessions, comparative pedagogical experiment. The main conclusions of the study are to substantiate the high pedagogical effectiveness of organized project-based learning in a professionally oriented educational space created on the basis of a social partnership between a university and an enterprise. The scientific novelty of the research lies in the application of project-based learning technologies in conditions of quasi-professional activity when students perform real projects. A special contribution of the authors to the study is the development of a model for the joint implementation of project-based learning technology in the preparation of students in terms of social partnership in the college-university-enterprise system.
An Evolutionary Fuzzy System for Multiclustering in Data Streaming

The rapid advance of technology has led to exponential growth in the generation of data in various fields, revolutionising how information is captured, processed and analysed. The continuous flow of data has proven to be a valuable resource for agile decision-making. The complexity of being able to make decisions from different sources has led to the emergence of new techniques that aim to obtain as much information as possible to relate data efficiently, taking into account multiple perspectives. A new approach to clustering streaming data from different perspectives is introduced in this paper. It is based on evolutionary algorithms (CHC), fuzzy rules and consensus function. The proposed approach is called FuzzyMultiCHCClust-DS. This approach addresses the clustering of streaming data by optimising the clustering process taking into account traditional clustering perspectives such as k-means, Clara, Pam, Fanny and MiniBatchKMeans. It has been tested using different sets of linguistic labels, achieving a high degree of compactness and separation of the clustered data.
SMS Spam Detection using Relevance Vector Machine

Spam in Short Message Service(SMS) is a serious issue that impacts mobile phone consumers all around the world. Many strategies have been applied using several deep learning and machine learning techniques to overcome these issues. The bagging approach is used in the study to combine four different algorithms, namely RVM, SVM, Naive Bayes, and KNN. Then the final prediction is calculated from the predictions obtained from each of these algorithms by using a majority-based voting approach. So, this paper offers research on the comparative analysis of various text classification algorithms for accurately detecting and classifying spam SMS messages. The dataset is first preprocessed and then vectorized using the TF-IDF method which gives more importance to the less frequent words rather than common words. The Relevance vector machine (RVM) implementation on the dataset, achieves the best performance on this dataset with an F1 score of 0.975175. According to the study's findings, the suggested RVM model may successfully categorize SMS spam messages and be applied in practical settings.
Phishing Detection using Gradient Boosting Classifier

Phishing attacks continue to pose a substantial and persistent threat to online security, jeopardizing the privacy and financial stability of individuals and organizations. The timely and accurate detection of these malicious websites remains an ongoing and critical challenge in the field of cybersecurity. In this research, we introduce an innovative approach aimed at addressing this issue through the utilization of Gradient Boosting Classifiers (GBCs). Phishing websites are specifically crafted to replicate the appearance of legitimate sites, with the primary intent of deceiving users into divulging sensitive information. Consequently, these malicious websites exhibit nuanced attributes that distinguish them from authentic counterparts. Traditional rule-based methods often encounter difficulties in effectively capturing these subtle distinctions. In contrast, Gradient Boosting offers an ensemble learning framework capable of harnessing the collective strength of weak classifiers, resulting in a robust model proficient at identifying these elusive features. Our experimental findings conclusively establish the superior performance of our proposed approach across various metrics, including accuracy, precision, recall, and the F1-score. Notably, our model demonstrates exceptional resilience against the adversarial tactics frequently employed to obscure the true nature of phishing websites. This research represents a significant step forward in the ongoing effort to fortify online security and protect users from the pervasive threat of phishing attacks.
Robust Framework for Malevolent URL Detection using Hybrid Supervised Learning

The In this Internet Era, most of the people are using world-wide-web (www) based websites extensively for accomplishing their daily activities including E-commerce. As per latest report, total of 1.5 billion URLs are accessed every day and it is increasing every second. From another perspective, malicious URLs are employed in URL phishing- which in turn steal customer credentials and thereby lead to loss of billions of dollars. Due to these aspects, malevolent URL detection is gaining huge attention in the literature in the past few years. However, the state-of-the art literature employ only popular ML techniques and small datasets with less diversity for detecting malicious URLs. Further, the existing malicious URL detection methods are less focusing on bottleneck issues such as Overfitting and hyper-parameter tuning of ML models, which play a significant role in deciding the prediction accuracy of the proposed detection framework. In order to tackle these issues, this research article proposes a robust Malevolent URL Detection framework, which utilizes a hybrid machine learning algorithm, Support Vector Regression (SVR) and hyper parameters tuning strategies to enhance the prediction accuracy. The experimental results conducted on training and testing datasets clearly demonstrate the performance of the proposed detection framework in terms of PR and accuracy metrics respectively.
QUESC: Environmental Sound classification Using Quantum Quantized Networks

Environmental Sound classification (ESC) is crucial in various fields such as audio surveillance, wildlife conservation, and acoustic monitoring. However, traditional methods often struggle to handle the curse of dimensionality and the limitations of classical machine learning algorithms. In the paper, a novel approach is proposed for ESC using a Quantized Hybrid Quantum-Classical Neural Network (QQNN), which leverages the potential advantages of quantum computing. The Hybrid QQNN architecture aims to reduce the required parameters and achieve performance comparable to traditional methods. It uses a set of pre-trained layers to reduce the number of features for the parameterized quantum circuit (PQC). This approach was evaluated using the ESC-10 dataset in Pennylane's environment to train the hybrid quantum model. The results demonstrate that the proposed Hybrid QQNN approach achieves comparable accuracy with the classical machine learning methods. This opens up the possibility of performing additional transfer learning in the future.
The Decentralized Smart Contract Certificate System Utilizing Ethereum Blockchain Technology

This research paper presents an innovative approach to address the challenges associated with platitudinous paper certificates and electronic certificates. These challenges include issues such as preservation, management, verification inconvenience, poor reliability, and susceptibility to counterfeiting and tampering. The suggested approach involves an autonomous certificate system leveraging blockchain technology and smart contracts. Tailored for university students engaged in innovation and entrepreneurship competitions, this platform is intended to offer blockchain-based certification services. Within this system, various functions related to certificates, such as management, issuance, verification, and revocation, are executed through smart contracts. Structured data within these contracts stores information about signers, certificate templates, and certificate details. This structure simplifies the process of querying and validating certificates, all the while maintaining their security and authenticity.