Reviewerly/exHarmony huggingface dataset:
Design Actions for the Design of Visualization Onboarding Methods

Integrating visualization onboarding methods into visual analytics tools presents challenges for designers and developers. These challenges include the varying complexity of visualization techniques, data types, and users’ expertise levels. Selecting and integrating educational theories, ensuring the completeness and clarity of onboarding instructions, choosing the appropriate medium, and determining interaction techniques for exploration during onboarding are also problematic. However, there needs to be established design guidance specifically focused on visualization onboarding. Existing resources like VisGuides and design patterns offer some qualitative advice but lack scientific foundations. To address this gap, we propose nine design actions based on critical reflections of empirical studies, the development of a design space, and user studies. These design actions emphasize customizing onboarding experiences to address users’ knowledge gaps, finding a balance between flexibility and structure, and incorporating concrete examples while considering potential limitations in knowledge transfer. Furthermore, we explore the connections, issues, and contradictions within the design actions. In conclusion, it is crucial to tailor onboarding experiences, balance flexibility and structure, and provide concrete examples while acknowledging knowledge transfer challenges.
Smart home lighting control environment

This paper explores the types of lighting, existing lighting models and available technologies. It deals with the execution of an addition for a prototype designed on the premises of university department (DCI, FEI, TUKE) in the form of a web interface that allows remote control and management by multiple users. Part of the solution is the design of an analytical module providing an overview of device usage in order to control the operation and further optimization to minimize energy consumption.
Comparison of Sentiment Classifiers on Slovak Datasets: Original versus Machine Translated

In this article, we focus on the comparison of classifiers on datasets in the Slovak language. In order to compare the effectiveness of the classifiers, we used two groups of data: the first group was obtained from the Internet and translated into the Slovak language, while the second group was originally in the Slovak language. Our goal was to find out how the translation of datasets affects the performance of classifiers and to identify the most suitable models for the Slovak language.
Educational Data Comics: What can Comics do for Education in Visualization?

This paper discusses the potential of comics for explaining concepts with and around data visualization. With the increasing spread of visualizations and the democratization of access to visualization tools, we see a growing need for easily approachable resources for learning visualization techniques, applications, design processes, etc. Comics are a promising medium for such explanation as they concisely combine graphical and textual content in a sequential manner and they provide fast visual access to specific parts of the explanations. Based on a first literature review and our extensive experience with the subject, we survey works at the respective intersections of comics, visualization and education: data comics, educational comics, and visualization education. We report on five potentials of comics to create and share educational material, to engage wide and potentially diverse audiences, and to support educational activities. For each potential we list, we describe open questions for future research. Our discussion aims to inform both the application of comics by educators and their extension and study by researchers.
Comparison of Deep Learning and Ensemble Learning in Classification of Toxic Comments

The paper is focused on recognition of various forms of toxic comments on social networks, particularly on the offensive and hate speech and on the cyberbullying. The spreading of toxic content through social networks is nowadays very serious problem, which can be harmful for a democratic society functioning. The paper presents an experiment with various machine learning methods to discover which from them would be the most suitable for building recognition models. Primarily, we will compare the deep learning and ensemble learning.
Quantum Information with Photons and Nanomaterials

Quantum Information is the branch of Science and Technology whose purpose is the exploitation of quantum phenomena for the processing and transmission of information [1]. Various technological platforms have been proposed for the transport and processing of quantum information and over the past years, systems based e.g. on superconducting devices, trapped ions, Rydberg atoms have been developed [2].
SUTD campusX: An Institutional Digital Transformation Approach to Higher Education

The recent Covid-19 pandemic has created a strong momentum for educational institutions across the world to transform their digital education. While online learning has its benefits and is valuable for distance and adult learning, there are several known limitations in terms of overall student satisfaction, social connectivity, student well-being, cost of transformation, student and faculty preparedness, and challenges in converting certain learning activities such as hands-on experiments and labs to be online. Our university, Singapore University of Technology and Design, uses signature student-centric pedagogies characterized by an interdisciplinary curriculum, learning and teaching in teams, design-centric project work, and assessments. This includes both Pre-employment Training (PET) and Continuing Education and Training (CET). Although existing technologies such as Zoom and TEAMS can be adapted easily for self-directed online courses such as MOOCs, the question is "How can we possibly use technology for student-centric teaching which involves interdisciplinary curriculum, and hands-on collaborative projects?" This requires both bottom-up and top-down changes in the university-level strategies for teaching and learning. Faculty members need to innovate teaching and learning, and university leadership, together with support services must strategize and present a sharp vision, clear directions, and supportive resources for moving this forward. To this end, our university has launched a campus-wide program to spur technology innovation for the ever-changing norm of teaching and learning. In this reflection paper, we will share our campusX initiative which focuses on cyber-physical learning to innovating digital learning.
Tool for gamifying teachers practices: first iteration

This article focuses on the creation of a support tool for teachers who wish to implement gamification in their teaching practices. In an environment where education must adapt to the changing demands of society, gamification presents itself as an active methodology that can increase student motivation and engagement. This research uses design-based research to identify the principles behind a tool that allows a teacher to include elements of gamification in their classrooms should have, for these different validations were performed with teachers and experts, allowing to recognize the following elements of gamification: progression, narrative, possibility of error, rules, meaningful choices and feedback, and the following design elements: ease of use, relevance, intuitive interaction and efficiency. Also, reinforce the relevance of the use of active methodologies such as gamification in the classroom.
Experimental Benchmarking of Quantum Machine Learning Classifiers

Quantum Machine Learning (QML) has emerged as an interdisciplinary field that harnesses the potential of quantum computing to revolutionize classical machine learning paradigms. This paper presents a benchmarking study focused on four quantum classifier algorithms: Quantum Support Vector Classifier (QSVC), Variational Quantum Circuit (VQC), Quantum Neural Network Classifier (QNNC) and Pegasos Quantum Support Vector Classifier (PegasosQSVC). These quantum classifiers hold promise for unlocking quantum advantages in classification tasks. However, a systematic assessment of their performance across diverse datasets is yet to be fully explored. In this research study, we evaluate and compare the classification performance of quantum machine learning classifiers on various datasets (Breast Cancer, Diabetes, and Titanic) and compare them to a classical model. Our findings revealed that PegasosQSVC consistently maintained high performance across all datasets with the highest average accuracy, recall, and F1-score values of 0.81, 0.72, and 0.71, respectively, followed by VQC, followed by VQC, while QSVC and QNNC perform variably. Nevertheless, QSVC was found to balance performance and training time, achieving an average accuracy of 0.81 and a training time of 177 seconds. Overall, the classical model surpassed QML models as it was up to 10% higher in average accuracy.
Quadrotor Experimental Dynamic Identification with Comprehensive NARX Neural Networks

This research paper delves into the field of quadrotor dynamics, which are famous by their nonlinearity, underactuation, and multivariable nature. Due to the critical need for precise modeling and control in this context we explore the capabilities of NARX (Nonlinear AutoRegressive with eXogenous inputs) Neural Networks (NN). These networks are employed for comprehensive and accurate modeling of quadrotor behaviors, take advantage of their ability to capture the hided dynamics. Our research encompasses a rigorous experimental setup, including the use of PRBS (Pseudo-random binary sequence) signals for excitation, to validate the efficacy of NARX-NN in predicting and controlling quadrotor dynamics. The results reveal exceptional accuracy, with fit percentages exceeding 99% on both estimation and validation data. Moreover, we identified the quadrotor dynamics using different NARX NN structures, including the NARX model with a sigmoid NN, NARX feedforward NN, and cascade NN. In summary, our study positions NARX-NN as a transformative tool for quadrotor applications, ranging from autonomous navigation to aerial robotics, thanks to their accurate and comprehensive modeling capabilities.
Transforming Healthcare: Raabin White Blood Cell Classification with Deep Vision Transformer

In the realm of computer vision, the application of deep learning techniques has brought about significant transformations, particularly in the domain of image classification. This study delves into an in-depth exploration of the Raabin White Blood Cell (WBC) dataset, leveraging the power of the Deep Vision Transformer (ViT), an advanced deep learning model, to advance the classification of white blood cells. Although the Raabin WBC dataset may be relatively lesser-known, its inherent value lies in its specialized focus on white blood cell images. To harness the potential of this dataset, we meticulously curated it for compatibility with DeepViT, followed by rigorous training and fine-tuning. Our efforts yielded exceptional results, with an impressive accuracy rate of 97%, thus surpassing previous benchmarks. This remarkable performance underscores the dataset’s significance in the context of medical image analysis, highlighting its potential for various applications, particularly in the field of medical research and diagnosis. Furthermore, this study serves as a compelling testament to the adaptability and efficacy of DeepViT when applied to specialized datasets. These groundbreaking results accentuate the importance of exploring lesser-known datasets in conjunction with advanced deep learning models, as they hold the promise of delivering exceptional performance in specific domains. This work paves the way for further research and applications in medical image analysis, setting a benchmark for future white blood cell classification studies.
Parasitic Signals: Multimodal Sonata for Real-time Interactive Simulation of the SARS-CoV-2 Virus

This project aims to transform the nano-scale of a striking biological phenomenon, the relationship between the SARS-CoV-2 virus and human molecules, into an interactive audiovisual simulation. In this work, Atomic Force Microscopy (AFM) touching and imaging a single molecule measures the interaction between the spike protein of SARS-CoV-2 and human cellular proteins and measures the dynamic of the spike protein. We create a comprehensive scientific model based on diverse datasets and theories presenting a real-time interactive complex system with efficient rendering and sonification using a single C++ platform. This project invites the audience into an immersive space where they can control the behavior of biomolecules, allowing them to intuitively perceive biological properties. This project is not only a demonstration of scientific data but also attempts to look at the interspecies relationship in parasitism which particularly deals with our current and post-pandemic life with coronavirus and how we might control our coexistence in a virtual space.
How important are Chatbots within Engineering Education? A literature-based review from 2011 to 2023

Over the years, the emergence of the fourth industrial revolution has brought a much-needed transition within the education sector. COVID-19 has contributed immensely on how teaching and learning is currently evolving. Most of the traditional face-to-face institutions found themselves utilizing technology to enhance teaching and learning activities. Technological tools brought amongst others, artificial intelligence that is currently dictating the direction of education system. Chatbots amongst others found its space within teaching and learning, allowing both students and academics to find education interesting. The current study evaluated the existence of Chatbots in engineering education fraternity by carrying out a literature-based review method focusing 2011 until recent. The findings of the study revealed that Chatbots were spotted as early as 2008 within the engineering education. It continued to make headways in various engineering modules such as Thermodynamics, Design Engineering, as well as in the administration of teaching and learning itself. In conclusion, the challenges and concerns associated with AI chatbots in education underscore the need for a nuanced and balanced approach. Ethical considerations, including data privacy and algorithmic bias, must be carefully navigated.
DiffT: A Novel Approach for Privacy Preserving Data Analytics

The use of data analytics has led to the creation of automated, accurate, and highly personalized customer experiences across various sectors. However, the use of personal data also exposes individuals to various privacy implications. This research project aims to investigate a technical approach to achieve data analytics without compromising individual privacy. The study in its first phase focused on three primary algorithms, k-anonymization, l diversity, and t-closeness, which are currently used to carry out anonymizations on datasets. It was observed that t-closeness had the least value of re-identification risk. This research aims to propose a novel approach for privacy preserving data analytics named "DiffT". We have enhanced existing techniques and then proposed a novel approach which is combination of both Differential Privacy and t-closeness. The proposed approach DiffT offers input privacy and output privacy control, making it a viable option for privacy-conscious organizations with less demanding computational needs. To benchmark and compare our findings with existing input privacy method Full Homomorphic Encryption, we devise Epsilon (ℇ), as Privacy Loss metric, which is assessed against the total privacy budget. The overall purpose of this research is to reduce the over-reliance on legal and organizational methods, compare the present privacy preserving analytics techniques, and propose a novel intersection of existing models.
AI ChatBots as Inherent Tools for Comprehensive Learning of Engineering Design: A Systematic review

The integration of artificial intelligence (AI) chatbots into education has opened new avenues for enhancing the learning experience, particularly in the field of engineering design. This systematic review critically examines the role of AI chatbots as an inherent tool for facilitating comprehensive learning in engineering design education, focusing on their personalized, interactive, and real-time support. Through an exhaustive analysis of existing literature, this review identifies the benefits, challenges, and potential implications of using AI chatbots to augment engineering design learning. The findings contribute valuable insights to educators, institutions, and policymakers seeking to harness the potential of AI chatbots for nurturing proficient engineering designers. By reshaping knowledge acquisition and skill development, AI chatbots can revolutionize engineering education and reshape the dynamics of knowledge acquisition and skill development.
The Risks Associated with AI Chatbots in Teaching Future Engineering Graduates: A Systematic Review

The educational system's trajectory has shifted dramatically in recent years. The introduction of chatbots and artificial intelligence (AI) techniques has mostly pushed the trend. Fundamentally, these methods are intended to improve the culture and experience of teaching and learning. However, there are risks associated with these technologies. The risks of using AI chatbots in the education of future engineering graduates are severely examined in this systematic review. This review identifies and addresses pedagogical, ethical, technological, and socio-cultural dangers that may come from the widespread use of AI chatbots based on an intensive review of the available research. The findings provide useful insights for educators, institutions, and policymakers as they manage the hurdles of implementing AI chatbots in engineering education responsibly.
The Impact and Outcomes of a VR Digital Plant for Enhanced Experiential Education across Academic Levels: Fostering High-Value Industry Competencies

RV Duck Toys" is a digital and virtual reality (VR) factory that allows students to access and experiment 24/7 inside a manufacturing plant, without the associated risks and with the possibility of operating some parameters of machines available, take data and observe the production process. This paper aims to share the benefits and results of the use of this digital and VR plant in a "learn by doing" learning strategy, where students can experience in a digital environment a setting that resembles a real plant.
Few-Shot Classification of Seals Based on Improved Ensemble Learning Algorithm

In enterprises, archives management is a repeatable and difficult work facing with tons of documents. Employees need to distinguish varieties of seals from thousands of materials. The task focuses on classifying which company's seal the material belongs to, so as to facilitate to put materials in an archive, rather than identifying the authenticity of seals. Ensemble learning of classical deep learning models can extract features from the image directly. And few shot learning method is applied to eliminate insufficiency and unbalance of samples, promoting the test accuracy from 62.2% to 96.2%. To promote the precision, a special voting method is proposed and the samples can be divided into known samples and unknown samples. About 90.95% of the samples belong to known samples and its precision arrived at 99.62% highly. This can satisfy the production and application of enterprises.
Review of Semi-Structured Document Information Extraction Techniques Based on Deep Learning

With the advent of global digital transformation, using an intelligent method based on deep learning to extract crucial information from semi-structured documents, as represented by various types of receipts and invoices, has emerged as an imperative measure to ensure business stability, data security, and improved work efficiency. This paper provides a detailed review on deep learning-based techniques for information extraction, with systematic introduction, hierarchical analysis, method comparison, and summary with expectations for future development. The review begins with a comprehensive explication of the defining characteristics of semi-structured documents, along with a detailed introduction to the research background, application areas, and technical challenges related to information extraction from semi-structured documents. Then the review extends to an overview of two developmental stages, i.e. the shift from traditional information extraction to deep learning-based information extraction, followed by discussion about technical architecture and method classification, which elaborates on key technologies in terms of typical datasets, detection and recognition, and information reduction. Lastly, paper summarizes the prospects and development in the field. Future research will focus on strengthening algorithm universal and lightweight, as well as improving information protection capabilities and the diversity of datasets.
A Novel Named Entity Recognition Mehod for Bus Route Identification in Social Media

With the rapid development of Internet, many people post their comments on smart bus services in social media. Some travelers' comments may contain bus route information. If bus route information can be identified, it is beneficial for regulatory personnel to troubleshoot all buses on the route, thereby improving the level of intelligent bus service. Therefore, the paper established a newly named entity recognition method to recognize bus route information. Firstly, the Bidirectional Encoder Representations from Transformer (BERT) model was used to encode the texts into vector representations. Then, multi Bidirectional Gated Recurrent Unit (MultiBiGRU) layers were used to improve the accuracy of feature extraction. Finally, the Conditional Random Fields (CRF) layer was used to calculate the global optimal result and the identification results of bus route information was obtained. The test results illustrated that the F1 value in the test set of the method reached 0.9389, and the recognition accuracy was higher than models such as CRF and BiGRU-CRF.
Chinese Long Text Summarization Generation Based on Multi-Task Re-Ordering and Smooth Gating Control

Abstractive summarization is a key technique for automatic text summarization. However, existing generative models typically rely on beam search decoding, which leads to poor performance due to the large search space. To address this, we propose a multi-task learning framework for text reordering. Specifically, we introduce a multi-task learning model to reorder the text according to different evaluation metrics, in order to select the optimal summary candidates. Furthermore, we replace the gating network in mixture-of-experts models with a smooth gating control method to alleviate the unsmooth parameter issue. To improve the ability of extracting semantic information, we incorporate textcnn into the basic encoder to enhance the semantic understanding of the model. Experiments on the Chinese long text summarization datasets CLTS and CLTS+ show significant improvements of our method over the best models reported in prior work, demonstrating the efficacy of the multi-task re-ordering framework and smooth gating control. Ablation studies are conducted to analyze the impact of different decoding methods and beam sizes, as well as the contribution of different re-ordering methods integrated into the framework.