We present GauKGT5, a sequence-to-sequence model proposed for knowledge graph completion (KGC). Our research extends the KGT5 model, a recent sequence-to-sequence link prediction (LP) model. GauKGT5 takes advantage of...
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Task allocation and scheduling methods are essential for edge-aided IoT to efficiently execute emerging computing-intensive deep neural network (DNN) applications. However, existing studies mainly overlook the waiting...
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In light of recent advancements in Internet of Multimedia Things (IoMT) and 5G technology, both the variety and quantity of data have been rapidly increasing. Consequently, handling zero-shot cross-modal retrieval (ZS...
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With the development of low-cost microcontrollers, sensors and wireless communications, intelligent environment monitoring based on the Internet of Things (IoT) has become possible. In this paper, an indoor environmen...
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ISBN:
(纸本)9798400709265
With the development of low-cost microcontrollers, sensors and wireless communications, intelligent environment monitoring based on the Internet of Things (IoT) has become possible. In this paper, an indoor environment monitoring system constructed by sensor network is realized. The system consists of distributed sensing nodes, which contain temperature and humidity, human infrared, voice recognition and camera sensors, etc. The nodes are connected to STM32 microcontrollers and WIFI modules and are scheduled in real time using free real time operating system(FreeRTOS). The visual and time series sensing data are transmitted to the server via WIFI in real time, and the user can monitor the system remotely by using the PYQT interface program on the PC. The system can also control home appliances by voice. The data interaction uses self-built WIFI module in AP mode for SOCKET communication. Through rigorous testing, the system demonstrates the capability of data communication, low-latency video streaming, and bidirectional control. The sensor network and real-time OS based architecture utilizes parallel execution to balance different temporal tasks and realize an agile cyber-physical system. This paper provides a reference for the underlying software infrastructure of intelligent buildings, embodying the integrated application of real-time scheduling, sensor networks and human-computer interaction.
Teacher-student learning has emerged as a promising framework for real-time video inference on mobile devices in multi-access edge computing (MEC) networks, where heavyweight teacher models are deployed on edge server...
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Due to their fast speed and easy deployment, Unmanned Aerial Vehicles (UAVs) have been widely used across various sectors, such as earthquake rescue, medical assistance, and smart agriculture. However, UAVs in deliver...
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Massive amounts of data are generated by sensor networks, edge computers, IoT devices, and enterprise networks. To process this volume of data requires (1) a scalable programming model that is not only concurrent and ...
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ISBN:
(纸本)9781665460873
Massive amounts of data are generated by sensor networks, edge computers, IoT devices, and enterprise networks. To process this volume of data requires (1) a scalable programming model that is not only concurrent and distributed, but supports the mobility of data and processes (actors), and (2) algorithms to distribute computations between nodes in a manner that improves overall performance while considering energy use in the system. With appropriate programming tools, we can distribute a given computation in a way that makes effective use of edge devices to improve performance while lowering energy consumption. The paper describes our work building on ideas based on the Actor model of computation. These include characterizing the relation of performance and energy consumption in parallel computation, and methods to support scalable placement mechanisms under dynamically changing network conditions and computational loads on edge devices. The paper will conclude with a presentation with a summary of open research problems.
In this paper, a convolutional neural network parameter training method based on Hausdorff difference is proposed to solve the problems of gradient vanishing and local optimum in the momentum algorithm. A momentum alg...
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Directory tree walks on parallel file systems are costly operations frequently required by many storage management tasks. Even listing the contents of a single directory can take minutes to hours for huge directories,...
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This paper presents a decentralized Deep Reinforcement Learning (DRL)-based dynamic channel bonding (DCB) algorithm (i.e., drlDCB) for Wi-Fi networks. Most existing RL-based channel bonding algorithms either are centr...
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