Generative models has been widely used for symbolic music generation. However, the quality of the music produced is hindered by the inadequate modeling of the harmonic and rhythmic relationships among various instrume...
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With the continuous advancement of human-computer interaction (HCI) technology, traditional interaction methods are increasingly unable to meet the growing complexity of application requirements. Human gesture recogni...
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ISBN:
(纸本)9798350377040;9798350377033
With the continuous advancement of human-computer interaction (HCI) technology, traditional interaction methods are increasingly unable to meet the growing complexity of application requirements. Human gesture recognition, as a natural and intuitive interaction method, has been widely applied in fields such as smart devices and virtual reality due to its convenience and flexibility. In recent years, the emergence of deep learning technology has provided new solutions for improving the performance of gesture recognition systems. This study designs a human gesture recognition and interaction system based on deep learning methods, aiming to enhance recognition accuracy and real-time responsiveness. First, the paper reviews the development of gesture recognition technology and provides a detailed analysis of deep learning-based gesture recognition methods. Subsequently, a gesture recognition model combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) is proposed, along with the overall system architecture. Through the processes of gesture image data collection, preprocessing, and model training, experimental results demonstrate that the proposed system exhibits significant advantages in recognition accuracy, robustness, and response speed. Finally, the paper discusses optimization strategies for the system and envisions the broad application prospects of deep learning technology in future HCI systems.
In the era of autonomous vehicles (AVs), ensuring the safety and security of the system is an ongoing challenge, particularly when faced with increasingly cyber-Attacks such as GPS spoofing and man-in-The-middle. This...
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Autonomous systems rely on artificial intelligence to perform their tasks more effectively. With the increasing complexity of tasks, it is essential to provide a structured way to define tasks. This paper explores a n...
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Scientific communities are increasingly using geographically distributed computing platforms. The current methods of compute placement predominantly use logically centralized controllers such as Kubernetes (K8s) to ma...
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With the increasing complexity of aircraft, document-basedsystemsengineering has gradually become inadequate, and model-basedsystemsengineering has become a hot issue in the aviation industry and academia. In view...
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As the demand for Machine Learning (ML)-based software continues to grow across various industries such as healthcare, automotive, energy, and banking, there is an increasing need for explainability requirements. Doma...
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High-Level Synthesis (HLS) Tools help engineers deal with the challenges of building complex systems that use reconfigurable technologies. In addition, HLS serves as a precursor to well-established methods in the soft...
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This position paper questions the need for systematically embedding AI technologies in interactive computing systems when other programming-based alternatives are available. Indeed, while AI technologies bring critica...
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ISBN:
(纸本)9783031592348;9783031592355
This position paper questions the need for systematically embedding AI technologies in interactive computing systems when other programming-based alternatives are available. Indeed, while AI technologies bring critical benefits in some contexts such as patterns recognition or computer vision, they also bring issues that prevent them from more generic use. For instance, their blackbox functioning calls for specific research on its explainability which, instead of solving the problems try to address issues brought both to developers (how does my Machine Learning system works) and users (why do I get this result). In addition, we demonstrate that these technologies are not mature enough yet to address reliability and dependability concerns in safety critical domains where probability of failures must be demonstrated to be in the order of magnitude of 10-E9. The paper thus argues for exploiting validated methods in the area of interactive systemsengineering to build interactive systems even though their exhibited behavior seems similar to the ones of AI-engineered systems.
In product design, a decomposition of the overall product function into a set of smaller, interacting functions is usually considered a crucial first step for any computer-supported design tool. Here, we propose a new...
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ISBN:
(纸本)9798350363029;9798350363012
In product design, a decomposition of the overall product function into a set of smaller, interacting functions is usually considered a crucial first step for any computer-supported design tool. Here, we propose a new approach for the decomposition of functions especially suited for later solutions based on Artificial Intelligence. The presented approach defines the decomposition problem in terms of a planning problem-a well established field in Artificial Intelligence. For the planning problem, logic-based solvers can be used to find solutions that compute a useful function structure for the design process. Well-known function libraries from engineering are used as atomic planning steps. The algorithms are evaluated using two different application examples to ensure the transferability of a general function decomposition.
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