The advancement of quantum communication technology is poised to revolutionize the current landscape of information transmission. Shor's algorithm is expected to break RSA encryption, which may challenge tradition...
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The challenges of conducting mining operations in open-pit mines, especially while attempting to maintain a stable residual wall, crucially relate to ensuring the safety of miners and preserving the landscape. Current...
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The EDITH project represents a significant advance in the application of machine learning in healthcare. Harnessing the power of massive medical data and sophisticated algorithms, EDITH aims to transform disease detec...
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One popular technology to improve the processing and storage capacities of vehicular networks (VNs) through the offloading of computing tasks is vehicular edge computing (VEC). Moreover, to provide better services for...
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ISBN:
(纸本)9781665462686
One popular technology to improve the processing and storage capacities of vehicular networks (VNs) through the offloading of computing tasks is vehicular edge computing (VEC). Moreover, to provide better services for users in proximity, microservices can be dynamically deployed, easily migrated among edge clouds on demand, and launched rapidly in a VEC environment. However, the environment of VNs is rapidly changing and unpredictable, making it difficult to provide service with low latency. Therefore, in order to deliver real-time services in microservice-enabled VNs, a multi-armed bandit (MAB) learning-based computation offloading (MLCO) strategy is introduced in this study. The proposed scheme enables that vehicles can learn the offloading delay performance of the candidates while offloading computing tasks. Furthermore, we modified the MAB algorithms and added an input-awareness strategy to our proposed algorithm for adapting to a rapidly changing task offloading vehicular environment. Extensive simulation results show that our proposal outperforms other existing baselines in terms of average service latency and successfully offloads more tasks in different scenarios.
The prediction of conformational B-cell epitope (CBCE) plays a crucial role in immunology research and is especially important for vaccine design. The laboratory experiments are too expensive and inefficient for CBCE ...
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Cloud computing is a game-changer model that opens new directions for modern manufacturing. It enables services and solutions that help improve the productivity and efficiency of smart production plants. The main obje...
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Cloud computing is a game-changer model that opens new directions for modern manufacturing. It enables services and solutions that help improve the productivity and efficiency of smart production plants. The main objective of the paper is to provide a summary of the various cloud-based manufacturing services currently being offered to manufacturers or that could be offered in the future. Additionally, the paper aims to discuss the various enabling technologies used to support the integration of cloud manufacturing in the manufacturing industry. Furthermore, the paper categorizes the different services based on their functionalities and maps them to four levels of production such as plant level, production line level, machine level, and process level. The categorization of services and mapping them to appropriate levels in production can enhance efficiency and productivity in the manufacturing industry. The study advances the discussion on cloud-based manufacturing from the types of services and enabling technologies perspective.
The purity of the water has recently been threatened by a number of contaminants. As a result, it is now crucial for the management of water pollution to model and anticipate water quality. In order to forecast the wa...
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The task of Named Entity Recognition (NER) involves the identification and categorization of entities in text through delineating their boundaries and assigning them to predefined categories. In recent years, grid tag...
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ISBN:
(纸本)9789819756711;9789819756728
The task of Named Entity Recognition (NER) involves the identification and categorization of entities in text through delineating their boundaries and assigning them to predefined categories. In recent years, grid tagging methods have shown a certain superiority in information extraction owing to the adaptable design of their model architecture. So, we adhere to the grid tagging method and propose the unified NER model, namely TCGA. Firstly, the unified NER task is modeled as a two-dimensional grid of words, and attention mechanism is applied to integrate position and region information. Secondly, the introduction of GRU and multi-scale convolutions aims to establish a finer-grained representation of words relationships, capturing internal dependencies and boundary information within phrases. The application of a collaborative predictor is ultimately utilized to effectively reason the intricate relationships between entities. We extensively conduct experiments on three widely recognized benchmark datasets, namelyCONLL03, GENIA, and CADEC, where the TCGA improves the SOTAF1-Scores results by approximately 2.79%, 2.61%, and 0.81%, respectively, demonstrating its effectiveness.
Sequential recommendation aims to predict users' future preferences by analyzing their historical interactions. Compared to traditional recommendation systems, sequential models emphasize capturing the dynamic cha...
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作者：
Zhang, ChenRen, HongruMa, HuiZhou, QiSchool of Automation
Guangdong University of Technology Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China School of Mathematics and Statistics
Guangdong University of Technology Guangzhou510006 China
This paper designs event-triggered switched observers for the networked distributed multi-agent systems based on the cloud computing systems. Firstly, a novel cloud computing system architecture is designed, in which ...
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