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A major obstacle in investigating the relationship between the anthropometric properties of an individual's pinnæ and their corresponding head-related transfer function (HRTF) is the inability to perform controlled experiments free from other confounding factors. We propose a method for performing these experiments by manufacturing a digitally-altered replica pinna. The process involves 3D-scanning a real pinna, performing the desired alterations, manufacturing the replica, and measuring the acoustic properties of the replica. Here, we describe our design and manufacturing methodology, 3D printing a mold of the altered pinna, which is then used to make a silicone casting, as well as our acoustic measurement apparatus and procedures. To demonstrate the effectiveness of our process, we manufactured an unaltered pinna replica and show that the replica has acoustic properties comparable to the original within the range of human hearing ( ≤ 20 kHz).

This book is the definitive guide to understanding web3 and NFTs, offering insights and guidance for individuals, educators and brands who are affected by this transformative technology.  As the world becomes increasingly digitised, non-fungible tokens (NFTs) offer a novel way to represent, authenticate and own unique digital assets providing new commerce opportunities for brands. Getting Started with web3 and NFTs supports professionals navigating this new field and provides guidance on operating within this space, best practices to follow and how to lead others in doing so too. This book also addresses the challenge of navigating the rapidly evolving landscape of NFTs and their impact on brands, consumers and society at large, while helping to provide new and curious consumers with advice on how best to stay safe.

The Indonesian government has now developed a QRIS EPayment for non-cash transaction between MSMEs and customers. MSMEs are the main channel for the success of the program through various transaction services provided to customers. This study aims to investigate the determine customers intention to adopt of QRIS e-Payment in Indonesia. Extended of UTAUT Theory will be used to investigate. Partial least squares structural equation modeling (PLS-SEM) was use to analyze the data. Using the purposive sampling technique, this study collected 195 respondents. We found that There are two main variables driving the customer in adopting of QRIS e-payment, namely social influence and facilitating conditions. Meanwhile, the variable performance expectancy and effort expectancy have a positive but insignificant effect. This result implies that In order to increase customer intention to use the QRIS e-payment system by consumers in Indonesia, QRIS service providers need to form a favorable opinion in society by increasing social influence through collaborating with community leaders or important people in their community and facilitating services that facilitate community adopts QRIS. This will encourage customer intention to adopt of QRIS e-payment and accelerate the migration of cash transactions to non-cash transactions in Indonesia.

User segmentation categorises a large and complex user base into manageable similar groups of users. Existing works encounter challenges when dealing with a sparse dataset and finding insights from the generated clusters. This study has two objectives: (1) to identify an optimal clustering model that can handle a sparse dataset and (2) to extract post-clustering insights via a descriptive persona for each cluster. This study deployed clustering models to handle a behavioural user-interaction dataset with a sparsity rate of 85%. The findings revealed that Density-Based Spatial Clustering of Applications with Noise that leveraged on One-hot Encoding and data representation learning via an autoencoder performed best, with a Silhouette score of 0.36. Subsequently, this study enacted techniques and tools such as classification, SHapley Additive exPlanation value, and manual analysis. Classification and SHAP values were used to identify important features that can differentiate clusters created by different clustering models. Specifically, a linear SHAP explainer object was applied to Logistic Regression had been identified to outperformed Random Forest and Light Gradient Boosting Machine, with an accuracy of 97%. A manual analysis of the central tendencies of these relatively more important features within each cluster was performed to create a descriptive persona. The findings revealed four distinctive personas, namely the \"Active User,\" \"COVID-19 Preventer,\" \"Inactive User,\" and \"Average Joe.\"

Skin cancer, a life-threatening disease, necessitates early detection and accurate classification for successful treatment. Misdiagnoses can lead to significant consequences for patients, highlighting the critical need for improved accuracy. Despite advancements in machine learning (ML) and deep learning (DL) techniques, accurate diagnosis remains challenging due to its complex nature. This comprehensive Systematic Literature Review (SLR) aims to examine the use of ML and DL models in skin cancer detection and classification, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transfer learning. Model performance is evaluated based on accuracy, sensitivity, specificity, and precision. Key findings reveal the dominance of DL models, with SVM-PSO emerging as a top-performing hybrid model with 97.50% accuracy. Tailored models, such as M-SVM and FCN-ResAlexNet, demonstrate high accuracy, emphasizing the importance of customization for dermatology tasks. Deep neural networks, such as ResNet-50, ResNet34, Inception V3, and ResNet 152, consistently exhibit strong performance, highlighting the impact of architectural depth. Traditional ML algorithms like Random Forest, KNN, and Naive Bayes face challenges compared to DL models. Furthermore, the analysis explores correlations between dataset size and accuracy, revealing varied model responses. Temporal trends and model-specific analyses uncover outliers, anomalies, and the influence of specific datasets (e.g. imbalanced classes), providing valuable insights for future research and model development. This multifaceted nature of model performance, influenced by factors beyond dataset size, underscores the need for nuanced considerations in dermatology image classification. Overall, the findings of this SLR offer valuable insights for researchers and practitioners, serving as a crucial step towards developing even more accurate and reliable tools for skin cancer diagnosis.

With the advent of the era of great intersection and integration, the development of generative artificial intelligence has caused the renewal of design methods, promoting a new paradigm of research in design fundamentals. The study seeks to investigate the research method of design form in the collaborative mode of artificial intelligence, to provide new ideas for design to conduct interdisciplinary research, and to promote design innovation under AI collaboration. This research begins with the design morphology theory, integrates interdisciplinary theories such as bionic design, and topology research, and collaborates with AIGC tools such as Midjourney, Stable Diffusion, and Chilloutmix to conduct case-specific research. To improve the accuracy of the morphological study, parametric design, bi-directional progressive topology optimization, genetic algorithm and simulation analysis, and other methods were also used in the research process to carry out a comprehensive design experiment exploration. This study also summarizes the AIGC prompt formula for the industrial design field and proposes an innovative seven-step design form research method with shape finding and shape making. This study also summarizes the AIGC prompt formula for the industrial design field and proposes an innovative seven-step design form research method with shape finding and shape making. Simultaneously, the pearl shell design morphology research is conducted in collaboration with AI technology, the full case design of the autonomous underwater vehicle is completed, and the efficacy of the seven-step design morphology research method is validated through fluid simulation. AI synergy provides new ideas for complex morphology research, extends and complements design, and plays a crucial role in the phases of morphology exploration, concept generation, and solution implementation, thereby assisting in the exploration of the central content of design morphology.

Over the last decade, rapid advances in information systems (ISs) have greatly reshaped and changed the nature of doing business and how its performance is measured, with Electronic Auditing (E-auditing) emerging as a pivotal element in improving organizational efficiency. This study addresses the challenges faced in manually implementing audits and underscores the necessity for transitioning to electronic audit systems. The manual approach has limitations regarding the accuracy of operations, so to enhance performance, E-auditing is now imperative. The purpose of the study is to evaluate E-auditing in the public sector of Saudi Arabia, utilizing DeLone and McLean’s information system model (DM ISM). The focus is on vital factors including information quality, system quality, service quality, system usage and user satisfaction and their influence on the performance of internal audit departments, particularly during the challenges posed by the recent COVID-19 pandemic. This research employs a quantitative approach, utilizing a self-administered survey questionnaire to collect data from E-auditing users in the Saudi public sector. The study applies partial least squares structural equation modelling (PLS-SEM) to validate the gathered data. Findings reveal that information quality and system quality significantly influence E-auditing usage. While service quality exhibits no marked effect on usage, the study establishes a strong relationship between E-auditing usage and user satisfaction. Effective E-auditing usage and satisfied users contribute convincingly to the improved performance of internal audit departments. The paper concludes with implications, limitations, and suggestions for future studies.

The Fourth Industrial Revolution (4IR) has been described as the next big leap in digital capitalism. Digital technologies such as artificial intelligence, quantum computing, 3D printing and robotisation, we are led to believe, will bring more progress, growth and development while also helping us to resolve the deep and multiple crises the world is in. Billions are being invested in these technologies, accompanied by sharp geopolitical rivalries to secure an edge in the control over them. Volume 8 in the Democratic Marxism series invites readers to think more deeply and critically about digital capitalism and its limits. While most governments in the world, including South Africa, have accepted a techno-nationalist narrative and have deliberated on the risks for the planet and humanity, the volume interrogates the effects and consequences of advances in artificial intelligence and heightened technological innovation and industrialisation on employment, democracy and the climate. Viewing the grand social engineering of 4IR through a Marxist lens, the volume contributors engage critically with the class project of digital monopoly capitalism and its powerful totalitarian tendencies. They question the dangerous technotopian imaginary shaping this digital techno-shift, the implications of algorithmic data extractivism, the securitisation of already weak market democracies, the social consequences of digital learning, lack of regulation, and the power dynamics in the labour process. Anchored in techno-realism, the interdisciplinary perspective captured in this volume puts forward alternatives for democratisation and a just transition to protect human and non-human life. ; The Fourth Industrial Revolution (4IR) has been vaunted as the next big leap in digital capitalism. Technologies such as artificial intelligence, quantum computing, 3D printing and robotisation mark this shift that promises not only more progress, growth and development but also solutions to the multiple crises the world is in. However, the billions being invested in these technologies are accompanied by sharp geopolitical rivalries to secure an edge in the control over them. Volume 8 in the Democratic Marxism series, Digital Capitalism and its Limits, questions the dangerous technotopian imaginary shaping this digital-techno shift to examine the risks and power dynamics involved. Contributors delve into the implications of algorithmic data extractivism, the securitisation of already weak market democracies, the social consequences of digital learning, regulatory lags and power dynamics in the labour process, as well as the possible emancipatory futures of such technologies. Anchored in techno-realism, this volume invites us all, from an interdisciplinary perspective, to think more deeply and critically about digital capitalism. We need to reject aspects of it in the public interest, and we may need to democratise it and subject it to a just transition to protect human and non-human life.

This cutting-edge resource introduces the basic concepts of passive bistatic radar, such as bistatic geometry, bistatic radar equation and analysis of different illuminating signals. These techniques, although known for almost a century, have not been developed intensively for decades, mainly due to technical limitations, but today, the passive radar concept can be realized in practice, and is of great interest for military and civilian users. This book provides insight into understanding the potential and limitations of passive radar systems, as well as the differences between signal processing in active and passive radar. Each of the signal processing stages typically applied in passive radar is described, including digital beamforming, clutter removal, target detection, localization and tracking. These concepts are illustrated with both simulated and measured data along with examples of passive radar systems.Correlation processing, which is crucial for passive radar operation, is presented, as well as practical approaches for calculating the cross-ambiguity function. The problems of range and velocity-cell migration are also introduced. The book analyzes and compares different antenna array geometries to show readers the appropriate solution for a particular scenario of passive radar. Cartesian tracking is also presented, based on the extended Kalman filter. Parallel and sequential updating approaches are introduced and compared. These concepts are illustrated with both simulated and measured data along with examples of passive radar systems, making this book useful for both novice and advanced practitioners.

Google Cloud Platform is a cloud computing service that offers hosting on the same supporting technology internally used by Google for its end users. This book follows a recipe-based approach, giving you hands-on experience to make the most out of Google Cloud services.