Leveraging Generative AI for Designing Next-Generation Haptic Systems

For efficient collaboration between participants, eye gaze is seen as being critical for interaction. Teleconferencing systems such as the AcessGrid allow users to meet across geographically disparate rooms but as of now there seems no substitute for face to face meetings. This paper gives an overview of some preliminary work that looks towards integrating eye gaze into an immersive Collaborative Virtual Environment and assessing the impact that this would have on interaction between the users of such a system. An experiment was conducted to assess the difference between users abilities to judge what objects an avatar is looking at with only head gaze being viewed and also with eye and head gaze data being displayed. The results from the experiment show that eye gaze is of vital importance to the subjects correctly identifying what a person is looking at in an immersive virtual environment. This is followed by a description of how the eye tracking system has been integrated into an immersive collaborative virtual environment and some preliminary results from the use of such a system.

Generative AI, a subset of artificial intelligence, is dedicated to the development of models that are capable of producing new content, including text, imagery, audio, and entire virtual worlds. Generative AI is capable of replicating human creativity and generating outputs that were previously considered to be exclusively the domain of human intelligence by utilizing sophisticated machine learning techniques, including deep learning and neural networks. AI models in the music industry are capable of composing music in a variety of genres, providing musicians and producers with new tools. Generative AI has the potential to stimulate innovation and creativity in a variety of disciplines as technology continues to develop. This research article delves deep into the various aspects of Generative AI namely history and core concepts, privacy and security, types of Generative AI models and their applications, NLP models and finally on Responsible AI.

Enhancing Library Management Software (LMS) through work automation, more efficient cataloging, and customized user experiences are all made possible by generative AI. In order to better understand how generative AI fits into library systems, this paper looks at how it may provide accurate information, optimize operations, and provide tailored suggestions depending on user preferences. AI-driven systems reduce the human labor necessary for categorization and resource management while boosting user engagement through targeted offerings. Notwithstanding these advantages, problems like prejudice in content produced by AI and worries about data privacy still exist.The integration of generative AI into Library Management Software (LMS) offers substantial potential for improving the efficiency and personalization of library services. Generative AI into LMS opens new opportunities to optimize functionality, improve user experience, and expand libraries digital outreach. The report underlines the necessity of addressing ethical issues and guaranteeing data transparency. It also highlights the necessity of scalable AI solutions, especially for smaller or less well-funded libraries. Libraries must overcome these obstacles as generative AI develops further in order to guarantee fair, efficient, and transparent AI inclusion.

Generative AI models represent a significant advancement in content creation capabilities but face substantial challenges when deployed at the network edge due to inherent resource constraints. This article examines comprehensive optimization strategies for enabling generative AI functionality on edge devices without requiring cloud connectivity. The exponential growth in model size has created a widening gap between computational requirements and the limited resources available in edge environments. Through systematic model compression, architectural redesign, and hardware-software co-optimization, generative models can achieve dramatic efficiency improvements while maintaining acceptable quality thresholds. The compression techniques examined include pruning methodologies that systematically eliminate redundant parameters, quantization approaches that reduce numerical precision, and knowledge distillation methods that transfer capabilities from larger models to compact alternatives. Architectural innovations such as modified attention mechanisms, conditional computation, and neural architecture search further enhance efficiency by fundamentally rethinking model design for resource-constrained environments. The integration of these techniques with hardware-specific optimizations and specialized software frameworks enables practical deployment across diverse application domains. Real-world implementations in speech processing, computer vision, and industrial IoT demonstrate that properly optimized generative models can operate within edge constraints while delivering near-real-time performance and maintaining high-quality outputs. These advancements empower industries to leverage generative AI capabilities in scenarios where privacy concerns, connectivity limitations, or latency requirements make cloud-based processing impractical.

Machine learning has surged in popularity in recent years, with deep neural networks (DNNs) becoming a cornerstone of applications such as autonomous driving, augmented reality, and natural language processing, owing to their remarkable accuracy and scalability. As the demand for greater portability and flexibility grows, there is a strong push to optimize these applications for resource-constrained edge devices like smartphones and IoT devices.However, deploying DNN models on such devices presents significant challenges, primarily due to the large size of these models, which is a key contributor to their high performance. For instance, the DeepSpeech 2 model outperforms its predecessor, DeepSpeech 1, but its size is eight times larger, encompassing approximately 68 million parameters. Similarly, other networks designed for various tasks, including convolutional neural networks like VGG and transformer-based networks like BERT and GPT, also exhibit substantial sizes. For example, the GPT-3 model, a predecessor to ChatGPT, consists of an astonishing 175 billion parameters. Consequently, fitting DNN models onto resource-limited edge devices is a formidable challenge. My thesis addresses these challenges with a three-part approach: * High-Performance DNN Accelerators: I will develop DNN accelerators that excel in high performance, low latency, and energy efficiency through an algorithm and hardware co-design across diverse FPGA architectures (Chapter 2). * Real-Time DNN Execution: I aim to achieve exceptional accuracy and real-time execution for various large-scale DNNs on off-the-shelf mobile devices, IoT devices, and other platforms (Chapter 3). * CompVQC Framework: I will explore a systematic framework, CompVQC, to reduce the circuit length of quantum neural networks (QNNs) through model compression, which can achieve up to a 20% improvement in accuracy. This reveals that compilation-aware compression can enhance the robustness of QNNs on near-term noisy quantum devices (Chapter 4). The field of AI has recently undergone a significant transformation, ushering in the era of generative AI, where models like ChatGPT and Stable Diffusion create realistic and creative content for a wide range of applications. As generative AI becomes more prevalent, it is crucial to adapt algorithm-hardware co-design strategies to meet the specific needs of these models, particularly on edge devices. In the final chapter (Chapter 5), I will introduce my ongoing and future work focused on activating generative AI on the edge.--Author's abstract

The central goal of linguistics-the science of human language-is understanding the following: why linguistic expressions are structured the way they are, how these structures represent meaning and externalized signal (sound for spoken languages, manual gestures for signed languages), how humans comprehend and produce these structures, and how humans acquire the mental capacities that make all of this possible (see, among many others, Chomsky, 1965 and Baker, 2002).By discovering the core underlying principles governing language, linguistics aims to 'carve nature at its joints.'Some of these principles may ostensibly be surface false in that they may not capture superficial surface patterns but instead reveal and explain deeper systematic phenomena.Consider, for example, the principle that allows language users to add relative clauses as modifiers of nouns, for example, the bracketed relative clause in the boy [the girl met].The principle, which appears to hold in almost every language, allows us-when stated properly-to replace the girl with any other expression of the same syntactic category, any other noun phrase, for example, the woman.However, this demand, in turn, is not surface true, as observed when considering the psychological difficulties associated with certain so-called center-embedding constructions.So, for example, replacing the girl with a more complex noun phrase such as the girl the woman likes yields a result that a speaker will never use and an addressee will find very difficult to understand: the boy [the girl the woman likes met]. 2 So, we have a fairly general principle with a host of considerations arguing that it is correctly stated, yet it yields results that appear to be false on the surface.The consensus has been that, despite this surface falsity, the principle is, nevertheless, correct in its idealized form (as part of the grammar internalized by a speaker of the language, speaker's competence).The principle appears to be sometimes false, according to this consensus, due to interfering factors (performance factors) pertaining to how speakers make use of the principles of grammar (of their competence) in producing and comprehending sentences in real time.This distinction between competence and performance is none other than the distinction between idealized theory and noise, familiar from high school physics (e.g., the distinction between the theory of motion and friction).And a consequence of this distinction is that there is a notion of 'being correct' for natural languages (being grammatical) that is importantly distinct from the notion of being 'probable' or 'expected.'No such competence-performance distinction (and no distinction between grammaticality and expectedness) is designed into current generative AI systems for language (see Yngve, 1960; Miller & Chomsky, 1963; and Chomsky, 1965; and in the context of LLMs, see Fox & Katzir, 2023; Katzir, 2023; and further in section 2.1).In contrast to some of the earlier works on generative modeling, which were driven by scientific understanding (Rumelhart & McClelland, 1986; Elman, 1993; Hinton, 2007), the development of modern generative AI technologies is largely use or performance driven: generative models of molecules can help scientists discover new drugs, image generation models allow nonexperts to create high-quality graphics at low cost, and LLMs enable computers to perform useful tasks involving language-such as answering a question given a user prompt.These models are generally trained on 'raw' data (e.g., pixels, words) and prioritize prediction over scientific understanding of the data domain.As an example, LLMs make use of enormous neural networks-

Copyright protection during the training stage of generative AI: Industry-oriented U.S. law, rights-oriented EU law, and fair remuneration rights for generative AI training under the UN's international governance regime for AI

<p><strong>Because of its wide range of applications, generative artificial intelligence (Generative AI) has received a lot of attention in academia and industry. Despite significant advances in computer vision and natural language processing, the use of Generative AI in tabular data is still underexplored. This gap is especially significant given the prevalence of tabular data as the primary data modality. This thesis seeks to close this gap by focusing on the efficient generation, evaluation, refinement, and application of tabular data, while addressing the challenges inherent in its heterogeneous nature.</strong></p><p>The primary goal of this thesis is to develop efficient algorithms for tabular data synthesis, advancing the field of Generative AI in the tabular domain. To achieve this goal, four specific research objectives have been outlined.</p><p>First, this thesis addresses gaps in evaluation metrics by unifying a framework for the comprehensive and consistent assessment of synthetic data. The proposed framework is designed to meet diverse business requirements across various downstream tasks by incorporating a wide range of advanced metrics. These metrics cover various types of evaluations, including univariate, bivariate, multivariate, cluster, and record-level evaluations. Additionally, standardized visualizations are provided to facilitate qualitative assessments. The results indicate that the proposed evaluation framework not only enables consistent comparisons, rankings, and the selection of different data synthesis approaches but also acts as a valuable tool to promptly assess the reliability of results obtained from synthetic data.</p><p>Second, this thesis presents innovative algorithms for the generation of tabular data, aiming to significantly enhance the quality of data synthesis. One of the contributions is the development of a reversible feature engineering pipeline designed to automatically represent tabular data in an efficient format while also ensuring that the transformed data can be easily converted back to its original format. Additionally, the thesis proposes novel deep learning-based tabular data generation models that are capable of learning the joint distribution of multivariate datasets without relying on predefined distribution assumptions. Experimental results highlight the effectiveness of the proposed algorithms in handling heterogeneous data, demonstrating superior performance in most scenarios within the same training duration when compared to similar alternatives.</p><p>Third, this thesis introduces innovative synthetic data prototype selection algorithms with the goal of refining the generated samples. This approach aims to leverage the advantages of Deep Generative Models, which, once trained, can generate unlimited and diverse synthetic data. By carefully identifying and selecting high-quality samples or removing unrealistic ones, the quality of the synthetic data can be enhanced from a post-processing perspective. Building on this hypothesis, we recognize the iterative nature inherent in the data synthesis procedure, where the processes of data generation, evaluation, and refinement should operate repeatedly in a cyclical flow. During the data generation phase, the evaluation framework assists in identifying potential issues and risks based on use cases, while the refinement (post-processing) step iteratively improves synthetic data in alignment with the evaluation outcomes.</p><p>Last, this thesis applies and validates the proposed models across various domains, including business, healthcare, and government, each requiring distinct downstream tasks. Specifically, we assess our algorithms from the standpoint of data balancing using churn data, evaluate our models focusing on data augmentation with health data, and test our algorithms from the perspective of data representation considering data privacy concerns within sensitive citizen data. These applications serve as practical illustrations, demonstrating the effectiveness and utility of the proposed Generative AI models in real-world scenarios.</p><p>In summary, this thesis makes a substantial contribution to the advancement of Generative AI within the domain of tabular data. It not only presents innovative algorithms and evaluation methods but also introduces practical frameworks applicable to real-world scenarios. The findings demonstrate the considerable capacity of Generative AI to fundamentally transform tabular data in various fields, ultimately leading to improved data availability, quality, and quantity.</p>

This chapter discusses the opportunities and challenges posed by the use of generative AI in the Sharia auditing profession. Generative AI (GAI) technologies have the potential to transform the operations of the financial and auditing departments completely. In a way, they perform self-importance roles such as building insights and automating challenging tasks while enhancing decision-making in these sectors. It is evident that through the use of GAI, the power of deep learning and neural networks can be harnessed to improve business processes and monitor outliers, which are critical for achieving both compliance and transparency in finance. However, we must highlight certain drawbacks that come along when employing GAI for Sharia-compliant auditing. According to Islamic law, Sharia compliance is based on Shariah rules and laws, so the audit has to meet certain requirements. Hence, auditing has to obey certain ethical and legal aspects.Does GAI integration into Sharia-compliant audits carry a concern of operating outside the technological, ethical, and Islamic principles? Yes, it has been observed that GAI systems depend on algorithmic geography, data tenderness, and the rules of regulation for their use. Besides, the GAI uptake exposes the increasing need for Sharia data and gaps in the interpretability of existing AI models. These are some of the reasons why GAI for Sharia auditors is attractive, even as there are challenges like automation of compliance and fraud detection, and a gap in operational definitions.This chapter explores the implication of GAI on Sharia auditors in terms of contemporary practice impact, challenges like ethics and technical limitations, and new opportunities for efficiency and compliance improvements. This discussion embeds insights from recent research, including works like Rabbani et al. on the necessity for ethical frameworks relating to AI in Islamic finance and Brown et al.’s findings about advanced language models in terms of their competency for actionable insight performance. Based on these aspects, this chapter will seek an all-rounded appreciation of the challenges and opportunities generative AI does or will pose.

An immersive virtual environment (IVE) modeling and simulation tool is being developed for designing advanced weapon and training systems. One unique feature of the tool is that the design, and not just visualization of the design is accomplished with the IVE tool. Acceptance of IVE tools requires comparisons with current commercial applications. In this pilot study, expert users of a popular desktop 3D graphics application performed identical modeling and simulation tasks using both the desktop and IVE applications. The IVE tool consisted of a head-mounted display, 3D spatialized sound, spatial trackers on head and hands, instrumented gloves, and a simulated speech recognition system. The results are preliminary because performance from only four users has been examined. When using the IVE system, users completed the tasks to criteria in less time than when using the desktop application. Subjective ratings of the visual displays in each system were similar. Ratings for the desktop controls were higher than for the IVE controls. Ratings of immersion and user enjoyment were higher for the IVE than for the desktop application. These results are particular remarkable because participants had used the desktop application regularly for three to five years and the prototype IVE tool for only three to six hours.

We describe a new method for adaptive model-based control of robotic dynamic systems using a new hybrid neuro-fuzzy-fractal approach. Intelligent control of robotic systems is a difficult problem because the dynamics of these systems is highly nonlinear. Optimal control of many robotic systems also requires methods which make use of predictions of future behavior. We describe an intelligent system for controlling robot manipulators to illustrate our neuro-fuzzy-fractal hybrid approach for adaptive control. We use a new fuzzy inference system for reasoning with multiple differential equations for model selection based on the relevant selection parameters for the problem. We use neural networks for identification and control of robotic dynamic systems.

Generative AI models have emerged as transformative tools in the healthcare domain, offering significant advances in disease detection, diagnosis, prognosis, and treatment planning. Although substantial progress has been made in the generation of 2D medical images, the synthesis of complex medical videos remains an unexplored area. The available literature on the generation of synthetic medical videos is minimal, highlighting a significant gap in this emerging area of research. This paper reviews the literature related to biomedical video synthesis using diffusion models and generative adversarial networks. The review aims to consolidate all relevant literature and highlight the different publicly available datasets, performance matrices, and the challenges associated with the generation of medical videos, along with some potential mitigation strategies. The findings of this review reveal that key challenges, such as maintaining temporal consistency, addressing computational inefficiencies, and overcoming data scarcity, are interconnected issues. Addressing these issues collectively is essential for the development of accurate and robust generative models tailored for medical video synthesis. The proposed potential mitigation strategies for the limitations of generative models in this review serve as a foundational resource for future research, aiming to enhance the reliability and applicability of generative AI models in clinical settings. These advances have the potential to significantly impact the domains of connected healthcare and personalized medicine by enabling the generation of realistic, high-quality medical video data that can enhance the training of diagnostic algorithms, improve the robustness of AI-assisted video interpretation, simulate disease progression or regression for more precise treatment planning, and support the development of personalized medicine techniques through enriched longitudinal data analysis.

We describe in this paper a new method for adaptive model-based control of robotic dynamic systems using a new hybrid approach combining neural networks, fuzzy logic, and genetic algorithms. Intelligent control of robotic systems is a difficult problem because the dynamics of these systems is highly non-linear. We describe an intelligent system for controlling robotic systems to illustrate our new hybrid approach for adaptive control. We use a new fuzzy inference system for reasoning with multiple differential equations for model selection based on the relevant parameters for the problem. In this case, the genetic algorithm is used to optimize the fuzzy system. We also use neural networks for identification and control of robotic systems.

In this pilot study, we aimed to explore the sense of presence and the physiological response evoked by an immersive virtual environment by using a modern head-mounted display (HMD) while performing a driving task in our simulator. We found an increase in mean whole session electrodermal activity (EDA) and heart rate (HR) as well as during emergency maneuvers events, showing a stronger response and more extended response when driving in the immersive simulation when compared with the standard simulation. At this proof of concept phase we were able to register and detect physiological signal differences between display modalities, suggesting deeper sense of presence when driving in an immersive environment.

Tourism is one of the most important sectors of the global economy, generating billions of euros in revenue and supporting millions of jobs. However, the tourism industry also faces many challenges, such as changing consumer preferences, environmental impacts and competitive pressures. In this paper, we explore how specialized and generative AI, a branch of artificial intelligence that can create novel content, such as text, images, or audio, can support hotels and other tourist accommodation companies in enhancing their products and services, improving their marketing and communication strategies and optimizing their operations and management. We review the existing literature on AI and its applications in tourism and propose potential use cases of generative AI for tourist companies. We also present some examples of generative AI tools that can be used for these purposes and discuss their benefits and limitations. Finally, we offer some conclusions and future implications for research and practice in this emerging field. Theoretical implications of the present paper suggest that the incorporation of generative AI in the tourism sector can significantly advance the current understanding of AI applications in service industries. By leveraging machine learning techniques, such as deep neural networks and generative adversarial networks, the tourism industry can create novel content and optimize various operations. This study enriches existing literature by identifying new use cases for generative AI, thus providing a foundation for further academic inquiry. Practically, our findings reveal several actionable insights for tourism companies. Generative AI tools can be harnessed to personalize customer experiences, enhance marketing efforts, and streamline operational management.