People resonate more with music when exposed to visual information, and music enhances their perception of video content. Cross-modal recommendation techniques can be used to suggest appropriate background music for a...
详细信息
This study aims to explore various methods for assessing the pointwise reliability of machine learning model predictions. We introduced and examined three metrics: 1) based on the concept of convex hull, 2) derived fr...
详细信息
Although the volume of stored data doubles every year, storage capacity costs decline only at a rate of less than 1/5 per year. At the same time, data is stored in multiple physical locations and remotely retrieved fr...
详细信息
ISBN:
(纸本)9798350322446
Although the volume of stored data doubles every year, storage capacity costs decline only at a rate of less than 1/5 per year. At the same time, data is stored in multiple physical locations and remotely retrieved from multiple sites. Thus, minimizing data storage costs while maintaining data fidelity and efficient retrieval is still a key challenge in database systems. In addition to the raw big data, its associated metadata and indexes equally demand tremendous storage that impacts the I/O footprint of data centers. In this vision paper, we propose a new signature-based compression (SIBACO) technique that is able to: (i) incrementally store big data in an efficient way;and (ii) improve the retrieval time for data-intensive applications. SIBACO achieves higher compression ratios by combining and compressing columns differently based on the type and distribution of data and can be easily integrated with column and hybrid stores. We evaluate our proposed tool using real datasets showing that SIBACO outperforms "monolithic" compression schemes in terms of storage cost.
Implantable medical devices (IMDs), such as pacemakers, neurostimulators, and drug delivery systems, are being revolutionized by advancements in radio frequency (RF)-based wireless power transfer (WPT). This study foc...
详细信息
Trajectory tracking for a differential drive robot is accomplished using Laguerre-based model predictive control. This paper emphasizes obstacle avoidance for the same robot using model predictive control. Simulation ...
详细信息
Object recognition systems have become integral in various domains, enhancing automation and decision-making processes. This research focuses on developing a cloud-based system specifically designed for efficient obje...
详细信息
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...
详细信息
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...
详细信息
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.
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...
详细信息
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...
详细信息
暂无评论